Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements

Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite materials as Silver Nanoparticles (AgNPs) into RWH sys...

Full description

Autores:
Ximo, Chen
Zhaojuan, Zhang
Abed, Azher M.
Luning, Lin
Haqi, Zhang
Escorcia Gutierrez, José
Shohan Ahmed, Ali A.
Elimam, Ali
Huiting, Xu
Hamid, Assilzadeh
Lei, Zhen
Tipo de recurso:
Article of investigation
Fecha de publicación:
2024
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/13539
Acceso en línea:
https://hdl.handle.net/11323/13539
https://repositorio.cuc.edu.co/
Palabra clave:
Rainwater harvesting (RWH)
Nano-composite materials
Silver nanoparticles
Machine learning regression
Energy efficiency in buildings
Environmental impact assessment
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
id RCUC2_4444168bc46b1a861d530a8f025b5476
oai_identifier_str oai:repositorio.cuc.edu.co:11323/13539
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements
title Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements
spellingShingle Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements
Rainwater harvesting (RWH)
Nano-composite materials
Silver nanoparticles
Machine learning regression
Energy efficiency in buildings
Environmental impact assessment
title_short Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements
title_full Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements
title_fullStr Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements
title_full_unstemmed Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements
title_sort Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements
dc.creator.fl_str_mv Ximo, Chen
Zhaojuan, Zhang
Abed, Azher M.
Luning, Lin
Haqi, Zhang
Escorcia Gutierrez, José
Shohan Ahmed, Ali A.
Elimam, Ali
Huiting, Xu
Hamid, Assilzadeh
Lei, Zhen
dc.contributor.author.none.fl_str_mv Ximo, Chen
Zhaojuan, Zhang
Abed, Azher M.
Luning, Lin
Haqi, Zhang
Escorcia Gutierrez, José
Shohan Ahmed, Ali A.
Elimam, Ali
Huiting, Xu
Hamid, Assilzadeh
Lei, Zhen
dc.subject.proposal.eng.fl_str_mv Rainwater harvesting (RWH)
Nano-composite materials
Silver nanoparticles
Machine learning regression
Energy efficiency in buildings
Environmental impact assessment
topic Rainwater harvesting (RWH)
Nano-composite materials
Silver nanoparticles
Machine learning regression
Energy efficiency in buildings
Environmental impact assessment
description Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite materials as Silver Nanoparticles (AgNPs) into RWH systems to elevate water treatment efficiency and assess the resulting environmental and energy-saving benefits. Utilizing a regression analysis approach with Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), this study will reach the study objective. In this study, the inputs are building attributes, environmental parameters, sociodemographic factors, and the algorithms SVM and KNN. At the same time, the outputs are predicted energy consumption, visual comfort outcomes, ROC-AUC values, and Kappa Indices. The integration of AgNPs into RWH systems demonstrated substantial environmental and operational benefits, achieving a 57% reduction in microbial content and 20% reductions in both chemical usage and energy consumption. These improvements highlight the potential of AgNPs to enhance water safety and reduce the environmental impact of traditional water treatments, making them a viable alternative for sustainable water management. Additionally, the use of a hybrid SVM-KNN model effectively predicted building energy usage and visual comfort, with high accuracy and precision, underscoring its utility in optimizing urban building environments for sustainability and comfort.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-10-25T22:07:45Z
dc.date.available.none.fl_str_mv 2024-10-25T22:07:45Z
dc.date.issued.none.fl_str_mv 2024-07-05
dc.type.none.fl_str_mv Artículo de revista
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.none.fl_str_mv Text
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.none.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coarversion.none.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_2df8fbb1
status_str publishedVersion
dc.identifier.citation.none.fl_str_mv Ximo Chen, Zhaojuan Zhang, Azher M. Abed, Luning Lin, Haqi Zhang, José Escorcia-Gutierrez, Ahmed Ali A. Shohan, Elimam Ali, Huiting Xu, Hamid Assilzadeh, Lei Zhen, Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements, Environmental Research, Volume 260, 2024, 119526, ISSN 0013-9351, https://doi.org/10.1016/j.envres.2024.119526.
dc.identifier.issn.none.fl_str_mv 0013-9351
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/13539
dc.identifier.doi.none.fl_str_mv 10.1016/j.envres.2024.119526
dc.identifier.instname.none.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.none.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.none.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Ximo Chen, Zhaojuan Zhang, Azher M. Abed, Luning Lin, Haqi Zhang, José Escorcia-Gutierrez, Ahmed Ali A. Shohan, Elimam Ali, Huiting Xu, Hamid Assilzadeh, Lei Zhen, Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements, Environmental Research, Volume 260, 2024, 119526, ISSN 0013-9351, https://doi.org/10.1016/j.envres.2024.119526.
0013-9351
10.1016/j.envres.2024.119526
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/13539
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.none.fl_str_mv Environmental Research
dc.relation.references.none.fl_str_mv Abbas, S., Mahmood, M.J., Yaseen, M., 2021. Assessing the potential for rooftop rainwater harvesting and its physio and socioeconomic impacts, Rawal watershed, Islamabad, Pakistan. Environ. Dev. Sustain. 23 (12), 17942–17963.
Abbasi, T., Abbasi, S., 2011. Sources of pollution in rooftop rainwater harvesting systems and their control. Crit. Rev. Environ. Sci. Technol. 41 (23), 2097–2167.
Abdipour, H., Hemati, H., 2023. Sonocatalytic process of penicillin removal usingFe2O3/effect of different parameters/degradation mechanism/kinetic study/ optimisation with response surface model. Int. J. Environ. Anal. Chem. 1–22.
Abdulla, F., Abdulla, C., Eslamian, S., 2021. Concept and technology of rainwater harvesting. Handbook of Water Harvesting and Conservation. basic concepts and fundamentals, pp. 1–16.
Aghamolaei, R., et al., 2018. Review of district-scale energy performance analysis: Outlooks towards holistic urban frameworks. Sustain. Cities Soc. 41, 252–264.
Akbarian, M., Najafi, E., Tavakkoli-Moghaddam, R., Hosseinzadeh-Lotfi, F., 2015. A Network-Based Data Envelope Analysis Model in a Dynamic Balanced Score Card. Mathemat. Problem Eng. 2015 (1), 914108.
Almeida, F.A.d., et al., 2022. Combining machine learning techniques with Kappa–Kendall indexes for robust hard-cluster assessment in substation pattern recognition. Elec. Power Syst. Res. 206, 107778.
Alvarez, A.N.R., Flores-De-la-Mota, I., Anguiano, F.I.S., 2024. Smart rainwater harvesting Service design. Procedia Comput. Sci. 232, 465–472.
Amasyali, K., El-Gohary, N.M., 2018. A review of data-driven building energy consumption prediction studies. Renew. Sustain. Energy Rev. 81, 1192–1205.
Anagnostopoulos, T., 2021. A predictive vehicle ride sharing recommendation system for smart cities commuting. Smart Cities 4 (1), 177–191.
Anayah, F.M., Almasri, M.N., 2009. Trends and occurrences of nitrate in the groundwater of the West Bank, Palestine. Appl. Geogr. 29 (4), 588–601.
Anoob, F., Meera, V., 2022. Optimization of operational parameters for the treatment of roof-harvested rainwater with biologically synthesised nanosilver coated on sand. Water Supply 22 (1), 1120–1130.
Anum, F., et al., 2023. Management of Botrytis Grey mold of tomato using bio-fabricated silver nanoparticles. South Afr. J. Bot. 159, 642–652.
Arabnejad Khanouki, Ramli Sulong, Shariati, M., 2011. Behavior of through beam connections composed of CFSST columns and steel beams by finite element studying. Adv. Mater. Res. 168, 2329–2333.
Arani, K.S., Zandi, Y., Pham, B.T., Mu’azu, M.A., Katebi, J., Mohammadhassani, M., Khorami, M., 2019. Computational optimized finite element modelling of mechanical interaction of concrete with fiber reinforced polymer. Comp. Concr. 23 (1), 61–68.
Armaghani, D.J., Mirzaei, F., Shariati, M., Trung, N.T., Shariati, M., Trnavac, D., 2020. Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber. Geomech. Eng. 20 (3), 191–205.
Asgari, G., Abdipour, H., Shadjou, A., 2023. A review of novel methods for Diuron removal from aqueous environments. Helyon.
Behzadian, K., et al., 2018. Can smart rainwater harvesting schemes result in the improved performance of integrated urban water systems? Environ. Sci. Pollut. Control Ser. 25, 19271–19282.
Bin Hariz, M., Said, D., Mouftah, H.T., 2021. A dynamic mobility Traffic model based on two modes of transport in smart cities. Smart Cities 4 (1), 253–270.
Boateng, E.Y., Otoo, J., Abaye, D.A., 2020. Basic tenets of classification algorithms Knearest-neighbor, support vector machine, random forest and neural network: a review. J. Data Anal. Inf. Process. 8 (4), 341–357.
Bourdeau, M., et al., 2019. Modeling and forecasting building energy consumption: a review of data-driven techniques. Sustain. Cities Soc. 48, 101533.
Bulut, S., Sahin, G., 2020. Pedagojik formasyon o¨grencilerinin ˘ su tüketim davranıs¸ları ile su ayak izlerinin incelenmesi. Akdeniz Üniversitesi Egitim ˘ Fakültesi Dergisi 3 (2), 53–70.
Carminati, M., et al., 2021. Miniaturized pervasive sensors for indoor health monitoring in smart cities. Smart Cities 4 (1), 146–155.
Castro, M., Jara, A.J., Skarmeta, A.F., 2013. Smart lighting solutions for smart cities. In: 2013 27th International Conference on Advanced Information Networking and Applications Workshops. IEEE.
Chahnasir, E.S., Zandi, Y., Shariati, M., Dehghani, E., Toghroli, A., Mohamad, E.T., Shariati, A., Safa, M., Wakil, K., Khorami, M., 2018. Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors. Smart Struct. Syst. 22 (4), 413–424.
Chen, L., Ng, E., 2012. Outdoor thermal comfort and outdoor activities: a review of research in the past decade. Cities 29 (2), 118–125.
Chen, J., Wang, H., Yin, W., Wang, Y., Lv, J., Wang, A., 2024. Deciphering carbon emissions in urban sewer networks: Bridging urban sewer networks with city-wide environmental dynamics. Water Res. 256, 121576. https://doi.org/10.1016/j.watres .2024.121576.
Chou, J.-S., Tran, D.-S., 2018. Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders. Energy 165, 709–726.
Copiaco, A., et al., 2023. An innovative deep anomaly detection of building energy consumption using energy time-series images. Eng. Appl. Artif. Intell. 119, 105775.
Daghara, A., Al-Khatib, I.A., Al-Jabari, M., 2019. Quality of drinking water from springs in Palestine: west bank as a case study. Journal of Environmental and Public Health 2019.
Dai, H., et al., 2024. Comparative assessment of two global sensitivity approaches considering model and parameter uncertainty. Water Resour. Res. 60 (2), e2023WR036096.
Dankovich, T.A., Gray, D.G., 2011. Bactericidal paper impregnated with silver nanoparticles for point-of-use water treatment. Environ. Sci. Technol. 45 (5), 1992–1998.
Dao, D.A., et al., 2021. Assessment of rainwater harvesting and maintenance practice for better drinking water quality in rural areas. J. Water Supply Res. Technol. - Aqua 70 (2), 202–216.
Davoodnabi, S.M., Mirhosseini, S.M., Shariati, M., 2021. Analyzing shear strength of steel-concrete composite beam with angle connectors at elevated temperature using finite element method. Steel Compos. Struct. 40 (6), 853–868..
Deb, C., et al., 2017. A review on time series forecasting techniques for building energy consumption. Renew. Sustain. Energy Rev. 74, 902–924.
Ehsan Shahabi, J.J.K., Maryam Barghi, 2023. Innovative Computational Approaches to Developing Sustainable Urban Infrastructure: Optimizing Green Roof Systems for Enhanced Water Management and Environmental Benefits. Intern. J. Civil Eng. Adv. 1 (1), 20–29.
Emad Toghroli, S.M., Fatemeh Moeini, Salman Maleki, 2023. Utilizing advanced machine learning algorithms for predicting the fatigue life of steel-reinforced concrete structures under variable load conditions. Intern. J. Civil Eng. Adv. 1 (1), 40–48.
Farah, E., Shahrour, I., 2017. Leakage detection using smart water system: Combination of water balance and automated minimum night flow. Water Resour. Manag. 31, 4821–4833.
Fei, Y., et al., 2023. Component design optimization of green roof substrate layer based on the assessment of multifunctional performance. Environ. Res. 238, 117190.
Ganaie, M.A., Tanveer, M., 2022. KNN weighted reduced universum twin SVM for class imbalance learning. Knowl. Base Syst. 245, 108578.
Ganji, F., et al., 2024. Evaluation of physical and chemical characteristics of wastewater and sludge of Zahedan urban wastewater treatment plant for reuse. Heliyon 10 (2), e24845.
Gbetkom, P.G., et al., 2024. Lake Tanganyika basin water storage variations from 2003–2021 for water balance and flood monitoring. Remote Sens. Appl.: Society and Environment 34, 101182.
Ge, J., Wang, Y., Zhou, D., Gu, Z., Meng, X., 2024. Effects of urban vegetation on microclimate and building energy demand in winter: An evaluation using coupled simulations. Sustain Cities Soc. 102, 105199. https://doi.org/10.1016/j.scs.2024.10 5199.
Georgi, B., et al., 2012. Urban Adaptation to Climate Change in Europe: Challenges and Opportunities for Cities Together with Supportive National and European Policies.
GhaffarianHoseini, A., et al., 2016. State of the art of rainwater harvesting systems towards promoting green built environments: a review. Desalination Water Treat. 57 (1), 95–104.
Goçer, ¨ O., ¨ Torun, A.O., ¨ Bakoviç, M., 2018. Kent dıs¸ı bir üniversite kampüsünün dıs¸ mekanlarında ˆ ısıl konfor, kullanım ve mekanˆ dizim analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 33 (3), 853–874.
Halhoul Merabet, G., et al., 2021. Intelligent building control systems for thermal comfort and energy-efficiency: a systematic review of artificial intelligence-assisted techniques. Renew. Sustain. Energy Rev. 144, 110969.
Hu, C., et al., 2023. Toward purifying defect feature for multilabel sewer defect classification. IEEE Trans. Instrum. Meas. 72, 1–11.
Hu, C., Dong, B., Shao, H., Zhang, J., Wang, Y., 2023. Toward Purifying Defect Feature for Multilabel Sewer Defect Classification. IEEE Trans. Instr. Measur. 72, 1–11. https://doi.org/10.1109/TIM.2023.3250306.
Huang, Z., et al., 2021. Integrated water resource management: rethinking the contribution of rainwater harvesting. Sustainability 13 (15), 8338.
Ismail, M., Shariati, M., Awal, A.A., Chiong, C.E., Chahnasir, E.S., Porbar, A., Heydari, A., Khorami, M., 2018. Strengthening of bolted shear joints in industrialized ferrocement construction. Steel Compos. Struct. 28 (6), 681–690.
Jain, P., Pradeep, T., 2005. Potential of silver nanoparticle-coated polyurethane foam as an antibacterial water filter. Biotechnol. Bioeng. 90 (1), 59–63.
Javan, K., et al., 2024. A review of interconnected challenges in the water–energy–food nexus: urban pollution perspective towards sustainable development. Sci. Total Environ. 912, 169319.
Judeh, T., Bian, H., Shahrour, I., 2021a. GIS-based spatiotemporal mapping of groundwater potability and palatability indices in arid and semi-arid areas. Water 13 (9), 1323.
Judeh, T., Shahrour, I., 2021b. Rainwater harvesting to address Current and forecasted Domestic water scarcity: application to arid and semi-arid areas. Water 13 (24), 3583.
Kamani, H., et al., 2023a. N-doped TiO2 nano particles for ultra violet photocatalytic degradation of coliform and fecal coliform from hospital wastewater effluent. Global NEST Journal 25 (2), 81–88.
Kamani, H., et al., 2023b. Synthesis of N-doped TiO2 nanoparticle and its application for disinfection of a treatment plant effluent from hospital wastewater. Desalination Water Treat. 289, 155–162.
Kamani, H., et al., 2024. Degradation of reactive red 198 dye from aqueous solutions by combined technology advanced sonofenton with zero valent iron: characteristics/ effect of parameters/kinetic studies. Heliyon 10 (1), e23667.
Kassani, S.H., Kassani, P.H., Najafi, S.E., 2018. Introducing a hybrid model of DEA and data mining in evaluating efficiency. Case Stud. Bank Bran arXiv preprint arXiv: 1810.05524.
Katebi, J., Shoaei-parchin, M., Shariati, M., Trung, N.T., Khorami, M., 2020. Developed comparative analysis of metaheuristic optimization algorithms for optimal active control of structures. Eng. Comput. 36, 1539–1558.
Kraus, B., et al., 2023. Improvement, digitalization and validation of a development method for enabling the utilization of sensory functions in design elements. Procedia CIRP 119, 272–277.
Krishna, H.J., et al., 2005. The Texas Manual on Rainwater Harvesting, third ed. Texas Water Development Board, Austin, Texas, United States of America.
Kyriazis, D., et al., 2013. Sustainable smart city IoT applications: heat and electricity management & Eco-conscious cruise control for public transportation. In: 2013 IEEE 14th International Symposium on" A World of Wireless, Mobile and Multimedia Networks"(WoWMoM). IEEE.
Lauerwald, R., et al., 2023. Impact of a large-scale replacement of maize by soybean on water deficit in Europe. Agric. For. Meteorol. 343, 109781.
Li, G., et al., 2018. Urban heat island effect of a typical valley city in China: responds to the global warming and rapid urbanization. Sustain. Cities Soc. 38, 736–745.
Li, Z., et al., 2023. Kinetic mechanisms of methane hydrate replacement and carbon dioxide hydrate reorganization. Chem. Eng. J. 477, 146973.
Lin, T.-P., Matzarakis, A., Hwang, R.-L., 2010. Shading effect on long-term outdoor thermal comfort. Build. Environ. 45 (1), 213–221.
Lin, X., et al., 2023. Stable precipitation isotope records of cold wave events in Eurasia. Atmos. Res. 296, 107070.
Liu, Z., et al., 2019. Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction. Energy Explor. Exploit. 37 (4), 1426–1451.
Liu, Z., et al., 2024. Calculation of carbon emissions in wastewater treatment and its neutralization measures: a review. Sci. Total Environ. 912, 169356.
Lu, S., et al., 2024. Influence of atmospheric circulation on the stable isotope of precipitation in the monsoon margin region. Atmos. Res. 298, 107131.
Luo, M., et al., 2020. Comparing machine learning algorithms in predicting thermal sensation using ASHRAE Comfort Database II. Energy Build. 210, 109776.
Luo, Z., Sinaei, H., Ibrahim, Z., Shariati, M., Jumaat, Z., Wakil, K., Khorami, M., 2019. Computational and experimental analysis of beam to column joints reinforced with CFRP plates. Steel Compos. Struct. 30 (3), 271–280.
Luo, J., Zhuo, W., Xu, B., 2023. A deep neural network-based assistive decision method for financial risk prediction in carbon trading market. J. Circ. Syst. Comput., 2450153
Luo, J., et al., 2024. The optimization of carbon emission prediction in low carbon energy economy under big data. IEEE Access.
Ma, M., et al., 2023. Comparative analysis on international construction and demolition waste management policies and laws for policy makers in China. J. Civ. Eng. Manag. 29 (2), 107–130.
Mahmoud, A.H.A., 2011. Analysis of the microclimatic and human comfort conditions in an urban park in hot and arid regions. Build. Environ. 46 (12), 2641–2656.
Makselon, J., et al., 2018. Role of rain intensity and soil colloids in the retention of surfactant-stabilized silver nanoparticles in soil. Environ. Pollut. 238, 1027–1034.
Martins, F., et al., 2021. A review of energy modeling tools for energy efficiency in smart cities. Smart Cities 4 (4), 1420–1436.
Mashhadi, N., et al., 2021. Use of machine learning for leak detection and localization in water distribution systems. Smart Cities 4 (4), 1293–1315.
McCarthy, P., 2024. Predicting trips to health care facilities: a binary logit and receiver operating characteristics (ROC) approach. Res. Transport. Econ. 103, 101411.
Melo, A.F.S., et al., 2024. Novel IEC 61850-based off-site engineering and validation methodology for protection, automation, and control systems. Elec. Power Syst. Res. 232, 110409.
Melville-Shreeve, P., Ward, S., Butler, D., 2016. Rainwater harvesting typologies for UK houses: a multi criteria analysis of system configurations. Water 8 (4), 129.
Mikelonis, A.M., Lawler, D.F., Passalacqua, P., 2016a. Multilevel modeling of retention and disinfection efficacy of silver nanoparticles on ceramic water filters. Sci. Total Environ. 566–567, 368–377.
Mikelonis, A.M., Lawler, D.F., Passalacqua, P., 2016b. Multilevel modeling of retention and disinfection efficacy of silver nanoparticles on ceramic water filters. Sci. Total Environ. 566, 368–377.
Mirzaei, P.A., 2015. Recent challenges in modeling of urban heat island. Sustain. Cities Soc. 19, 200–206.
Mittelman, A.M., et al., 2015. Silver dissolution and release from ceramic water filters. Environ. Sci. Technol. 49 (14), 8515–8522.
Mohammad, M.T.F., 2023. Advanced computational techniques for the assessment of wind load impact on high-rise building structures. Intern. J. Civil Eng. Adv. 1 (1), 49–57.
Mohammadhassani, M., Nezamabadi-Pour, H., Suhatril, M., Shariati, M., 2013. Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams. Struct. Eng. Mech. 46 (6), 853–868.
Mohammadhassani, M., Nezamabadi-Pour, H., Suhatril, M., Shariati, M., 2014. An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups. Smart Struct. Syst. Int. J. 14 (5), 785–809.
Mohammadhassani, M., Suhatril, M., Shariati, M., Ghanbari, F., 2013. Ductility and strength assessment of HSC beams with varying of tensile reinforcement ratios. Struct. Eng. Mech. 48 (6), 833–848.
Momeni, E., Lotfi, F.H., Saen, R.F., Najafi, E., 2019. Centralized DEA-based reallocation of emission permits under cap and trade regulation. J. Clean Prod. 234, 306–314.
Moradzadeh, A., et al., 2021. Heating and cooling loads forecasting for residential buildings based on hybrid machine learning applications: a comprehensive review and comparative analysis. IEEE Access 10, 2196–2215.
Morteza Shariati, M.H., Asma Mohammadi Pour, 2023. Evaluating the use of recycled glass in concrete mixtures: A comprehensive strength and durability analysis using neural networks for mix ratio optimization. Intern. J. Civil Eng. Adv. 1 (1), 30–39.
Mosavi, A., Bahmani, A., 2019. Energy consumption prediction using machine learning; a review.
Mudumbe, M.J., Abu-Mahfouz, A.M., 2015. Smart water meter system for user-centric consumption measurement. In: 2015 IEEE 13th International Conference on Industrial Informatics (INDIN). IEEE.
Naghipour, M., Niak, K.M., Shariati, M., Toghroli, A., 2020. Effect of progressive shear punch of a foundation on a reinforced concrete building behavior. Steel Compos. Struct. 35 (2), 279–294.
Naveen Kumar, Kattimani, S., Marques, F.D., Nguyen-Thoi, T., Shariati, M., 2023. Geometrically nonlinear study of functionally graded saturated porous plates based on refined shear deformation plate theory and biot’s theory. Intern. J. Struct. Stab. Dyn. 23 (02), 2350013.
Nawaz, M., et al., 2012. Silver disinfection of Pseudomonas aeruginosa and E. coli in rooftop harvested rainwater for potable purposes. Sci. Total Environ. 431, 20–25.
Niu, Y.-L., et al., 2024. Global climate change: effects of future temperatures on emergency department visits for mental disorders in Beijing, China. Environ. Res., 119044
Ozeren, ¨ O., ¨ Kayili, M.T., 2021. Designing public squares to optimize human outdoor thermal comfort: a case study in Safranbolu. Journal of Awareness 6 (1), 13–20.
Palermo, S.A., Talarico, V.C., Pirouz, B., 2020. Optimizing rainwater harvesting systems for non-potable water uses and surface runoff mitigation. In: Numerical Computations: Theory and Algorithms: Third International Conference, NUMTA 2019, Crotone, Italy, June 15–21, 2019, Revised Selected Papers, Part I 3. Springer.
Pan, J., Deng, Y., Yang, Y., Zhang, Y., 2023. Location-allocation modelling for rational health planning: Applying a two-step optimization approach to evaluate the spatial accessibility improvement of newly added tertiary hospitals in a metropolitan city of China. Soc. Sci. Med. 338, 116296. https://doi.org/10.1016/j. socscimed.2023.116296.
Pasika, S., Gandla, S.T., 2020. Smart water quality monitoring system with cost-effective using IoT. Heliyon 6 (7), e04096.
PCBS, 2018. Quantity of Water Supply for Domestic Sector, Water Consumed and Daily Consumption Per Capita in the West Bank by Governorate in.
Peng, J., Yan, G., Zandi, Y., Agdas, A.S., Pourrostam, T., El-Arab, I.E., Khadimallah, M.A., 2022. Prediction and optimization of the flexural behavior of corroded concrete beams using adaptive neuro fuzzy inference system. Struct. 43, 200–208.
Petkovi´c, B., Zandi, Y., Agdas, A.S., Nikoli´c, I., Deni´c, N., Koji´c, N., Khan, A., 2022. Adaptive neuro fuzzy evaluation of energy and non-energy material productivity impact on sustainable development based on circular economy and gross domestic product. Business Strat. Environ. 31 (1), 129–144.
Petrolo, R., Loscri, V., Mitton, N., 2017. Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms. Transactions on emerging telecommunications technologies 28 (1), e2931.
Plumbers, M., Mechanical Services Association of Australia, 2008. Rainwater Tank Design and Installation Handbook.
Prasad, A., et al., 2015. Smart water quality monitoring system. In: 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). IEEE.
Preeti, P., Rahman, A., 2021. A case study on reliability, water demand and economic analysis of rainwater harvesting in Australian capital cities. Water 13 (19), 2606.
Qian, Y., et al., 2024. Anaerobic oxidation of diclofenac coupled with dissimilatory iron reduction: Kinetics, mechanism, and microbial community function succession. Chem. Eng. J. 489, 151027.
Ramokone, A., et al., 2021. A review on behavioural propensity for building load and energy profile development – model inadequacy and improved approach. Sustain. Energy Technol. Assessments 45, 101235.
Ramos, H.M., et al., 2019. Smart water management towards future water sustainable networks. Water 12 (1), 58.
Ranaee, E., et al., 2021. Feasibility of rainwater harvesting and consumption in a middle eastern semiarid urban area. Water 13 (15), 2130.
Ranjan, V., et al., 2020. The Internet of Things (IOT) based smart rain water harvesting system. In: 2020 6th International Conference on Signal Processing and Communication (ICSC). IEEE.
Rasekh, A., et al., 2016. Smart water networks and cyber security. American Society of Civil Engineers, 01816004.
Razavian, L., Naghipour, M., Shariati, M., Safa, M., 2020. Experimental study of the behavior of composite timber columns confined with hollow rectangular steel sections under compression. Struct. Eng. Mech. 74 (1), 145–156.
Richards, S., et al., 2021. Sustainable water resources through harvesting rainwater and the effectiveness of a low-cost water treatment. J. Environ. Manag. 286, 112223.
Robles, T., et al., 2014. An internet of things-based model for smart water management. In: 2014 28th International Conference on Advanced Information Networking and Applications Workshops. IEEE.
Rostad, N., Foti, R., Montalto, F.A., 2016. Harvesting rooftop runoff to flush toilets: Drawing conclusions from four major US cities. Resour. Conserv. Recycl. 108, 97–106.
Safa, M., Maleka, A., Arjomand, M.A., Khorami, M., Shariati, M., 2019. Strain rate effects on soil-geosynthetic interaction in fine-grained soil. Geomech. Eng. 19 (6), 533–542.
Safa, M., Sari, P.A., Shariati, M., Suhatril, M., Trung, N.T., Wakil, K., Khorami, M., 2020. Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes. Phys. A: Stat. Mech. Appl. 550, 124046.
Safa, M., Shariati, M., Ibrahim, Z., Toghroli, A., Baharom, S.B., Nor, N.M., Petkovi´c, D., 2016. Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel Compos. Struct. 21 (3), 679–688.
Samadi, M.T., et al., 2024. The utility of ultraviolet beam in advanced oxidationreduction processes: a review on the mechanism of processes and possible production free radicals. Environ. Sci. Pollut. Res. Int. 31 (5), 6628–6648.
Satani, S., 2024. Nano-Material Enhanced Rainwater Harvesting Surfaces.
Savi´c, D., Vamvakeridou-Lyroudia, L., Kapelan, Z., 2014. Smart meters, smart water, smart societies: the iWIDGET project. Procedia Eng. 89, 1105–1112.
Sayed, A.N., Himeur, Y., Bensaali, F., 2022. Deep and transfer learning for building occupancy detection: a review and comparative analysis. Eng. Appl. Artif. Intell. 115, 105254.
Sedghi, Y., Zandi, Y., Shariati, M., Ahmadi, E., Azar, V.M., Toghroli, A., Wakil, K., 2018. Application of ANFIS technique on performance of C and L shaped angle shear connectors. Smart Struct. Syst. 22 (3), 335–340.
Sehgal, M., Goyal, S., Kumar, S., 2022. Comparative study on energy-efficiency for Wireless Body area network using machine learning approach. In: 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE.
Shahsavari-Pour, N., Modarres, M., Tavakoli-Moghadam, R., Najafi, E., 2010. Optimizing a multi-objectives time-cost-quality trade-off problem by a new hybrid genetic algorithm. World Appl. Sci. J. 10 (3), 355–363.
Shang, M., Luo, J., 2021. The tapio decoupling principle and key strategies for changing factors of Chinese urban carbon footprint based on cloud computing. Int. J. Environ. Res. Publ. Health 18 (4), 2101.
Shanmugasundharam, A., et al., 2023. Water quality index (WQI), multivariate statistical and GIS for assessment of surface water quality of Karamana river estuary, west coast of India. Total Environment Research Themes 6, 100031.
Shariati, M., Afrazi, M., Kamyab, H., Rouhanifar, S., Toghroli, E., Safa, M., Afrazi, H., 2024. A state of the art review on geotechnical reinforcement with end life tires. Glob. J. Environ. Sci. Manag. 10 (1), 385–404.
Shariati, M., Azar, S.M., Arjomand, M.A., Tehrani, H.S., Daei, M., Safa, M., 2020. Evaluating the impacts of using piles and geosynthetics in reducing the settlement of fine-grained soils under static load. Geomech. Eng. 20 (2), 87–101.
Shariati, M., Davoodnabi, S.M., Toghroli, A., Kong, Z., Shariati, A., 2021. Hybridization of metaheuristic algorithms with adaptive neuro-fuzzy inference system to predict load-slip behavior of angle shear connectors at elevated temperatures. Compos. Struct. 278, 114524.
Shariati, M., Ghorbani, M., Naghipour, M., Alinejad, N., Toghroli, A., 2020. The effect of RBS connection on energy absorption in tall buildings with braced tube frame system. Steel Compos. Struct. 34 (3), 393–407.
Shariati, M., Grayeli, M., Shariati, A., Naghipour, M., 2020. Performance of composite frame consisting of steel beams and concrete filled tubes under fire loading. Steel Compos. Struct. 36 (5), 587–602.Chicago.
Shariati, M., Mafipour, M.S., Ghahremani, B., Azarhomayun, F., Ahmadi, M., Trung, N. T., Shariati, A., 2022. A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement. Eng. Comp. 1–23.
Shariati, M., Mafipour, M.S., Haido, J.H., Yousif, S.T., Toghroli, A., Trung, N.T., Shariati, A., 2020. Identification of the most influencing parameters on the properties of corroded concrete beams using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Steel Compos. Struct. 34 (1), 155.
Shariati, M., Raeispour, M., Naghipour, M., Kamyab, H., Memarzadeh, A., Nematzadeh, M., Toghroli, A., 2024. Flexural behavior analysis of double honeycomb steel composite encased concrete beams: An integrated experimental and finite element study. Case Stud. Constr. Mater. 20, e03299.
Skvortsov, A.N., Ilyechova, E.Y., Puchkova, L.V., 2023. Chemical background of silver nanoparticles interfering with mammalian copper metabolism. J. Hazard Mater. 451, 131093.
Sły´s, D., Stec, A., 2020. Centralized or decentralized rainwater harvesting systems: a case study. Resources 9 (1), 5
Soni, A., Das, P.K., Yusuf, M., Ridha, S., Kamyab, H., Alam, M.A., Prakash, C., 2023. Synergy of silica sand and waste plastics as thermoplastic composites on abrasive wear characteristics under conditions of different loads and sliding speeds. Chemosphere 323, 138233.
Sotiropoulou, K.F., Vavatsikos, A.P., Botsaris, P.N., 2024. A hybrid AHP-PROMETHEE II onshore wind farms multicriteria suitability analysis using kNN and SVM regression models in northeastern Greece. Renew. Energy 221, 119795.
Stratigea, A., 2012. The concept of ‘smart cities’. Towards community development? Netcom. R´eseaux, communication et territoires (26–3/4), 375–388.
Tamagnone, P., et al., 2020. Rainwater harvesting techniques to face water scarcity in african drylands: hydrological efficiency assessment. Water 12 (9), 2646.
Tiana, S.J., 2023. Comprehensive predictive modeling of earthquake resilience in multistory buildings utilizing advanced machine learning techniques. Intern. J. Civil Eng. Adv. 1 (1), 10–19.
Toghroli, A., Mohammadhassani, M., Suhatril, M., Shariati, M., Ibrahim, Z., 2014. Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel. Compos. Struct. 17 (5), 623–639.
Toghroli, A., Nasirianfar, M.S., Shariati, A., Khorami, M., Paknahad, M., Ahmadi, M., Zandi, Y., 2020. Analysis of extended end plate connection equipped with SMA bolts using component method. Steel Compos. Struct. 36 (2), 213–228.
Uddin, M.G., et al., 2022. A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment. Water Res. 219, 118532.
Vishnu, S., et al., 2021. IoT-Enabled solid waste management in smart cities. Smart Cities 4 (3), 1004–1017.
Vladimirovaa, K., Le Blanc, D., 2015. How Well Are the Links between Education and Other Sustainable Development Goals Covered in UN Flagship Reports?: A Contribution to the Study of the Science-Policy Interface on Education in the UN System (October 2015).
Wehbe, R., Shahrour, I., 2021. A bim-based smart system for fire evacuation. Future Internet 13 (9), 221.
Wu, Z.Y., El-Maghraby, M., Pathak, S., 2015. Applications of deep learning for smart water networks. Procedia Eng. 119, 479–485.
Wei, Z., Zandi, Y., Gholizadeh, M., Selmi, A., Roco-Videla, A., Konbr, U., 2021. On the optimization of building energy, material, and economic management using soft computing. Adv. Concr. Constr. 11 (6), 455–468.
Wu, K., et al., 2024. Water rights trading planning and its application in water resources management: a water-ecology-food nexus perspective. Environ. Res., 118377
Xu, J., et al., 2023. Urban rainwater utilization: a review of management modes and harvesting systems. Front. Environ. Sci. 11.
Yaacoub, A., Esseghir, M., Merghem-Boulahia, L., 2023. A review of different methodologies to study occupant comfort and energy consumption. Energies 16 (4), 1634.
Yazdani, M., Kabirifar, K., Frimpong, B.E., Shariati, M., Mirmozaffari, M., Boskabadi, A., 2021. Improving construction and demolition waste collection service in an urban area using a simheuristic approach: A case study in Sydney, Australia. J. Clean Prod. 280, 124138.
Yousef Zandi, A.S.A., Ramezani, M., 2023. Advanced Integration of IoT and Neural Networks for Real-Time Structural Health Monitoring and Assessment of Bridges. Intern. J. Civil Eng. Adv. 1 (1), 1–9.
Yu, Y., et al., 2021. Producing and storing self-sustaining drinking water from rainwater for emergency response on isolated island. Sci. Total Environ. 768, 144513.
Zainah, T.A.S.M.I., Shahaboddin, S.M.S.M.S., 1801. Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. J. Intellig. Manuf. 29 (8), 1793.
Zandi, Y., Burnaz, O., Durmus¸, A., 2012. Determining the temperature distributions of fire exposed reinforced concrete cross-sections with different methods. Res. J. Environ. Earth Sci. 4 (8), 782–788.
Zendehboudi, A., Baseer, M.A., Saidur, R., 2018. Application of support vector machine models for forecasting solar and wind energy resources: a review. J. Clean. Prod. 199, 272–285.
Zhang, L., et al., 2021. A review of machine learning in building load prediction. Appl. Energy 285, 116452.
Zhang, Y., et al., 2023. Fermentative iron reduction buffers acidification and promotes microbial metabolism in marine sediments. J. Environ. Chem. Eng. 11 (5), 110922.
Zhang, F., Qian, H., 2024. A comprehensive review of the environmental benefits of urban green spaces. Environ. Res., 118837
Zhang, R., Yin, L., Jia, J., Yin, Y., Li, C., 2019. Application of ATS-GWIFBM operator based on improved time entropy in green building projects. Adv. Civil Eng. 2019, 3519195. https://doi.org/10.1155/2019/3519195.
Zhao, J., et al., 2021. IoT-based sanitizer station network: a facilities management case study on monitoring hand sanitizer dispenser usage. Smart Cities 4 (3), 979–994.
Zheng, X., et al., 2023. Rainwater harvesting for agriculture development using multiinfluence factor and fuzzy overlay techniques. Environ. Res. 238, 117189.
Zheng, L., Deng, Y., 2024. Advancing rainwater treatment technologies for irrigation of urban agriculture: a pathway toward innovation. Sci. Total Environ. 916, 170087.
Zhou, P., Peng, R., Xu, M., Wu, V., Navarro-Alarcon, D., 2021. Path Planning With Automatic Seam Extraction Over Point Cloud Models for Robotic Arc Welding. IEEE Robot Autom. Lett. 6 (3), 5002–5009. https://doi.org/10.1109/LRA.2021.3070828.
Zhu, G., et al., 2022. Evaporation, infiltration and storage of soil water in different vegetation zones in the Qilian Mountains: a stable isotope perspective. Hydrol. Earth Syst. Sci. 26 (14), 3771–3784.
dc.relation.citationendpage.none.fl_str_mv 19
dc.relation.citationstartpage.none.fl_str_mv 1
dc.relation.citationissue.none.fl_str_mv 119526
dc.relation.citationvolume.none.fl_str_mv 260
dc.rights.none.fl_str_mv © 2024 Published by Elsevier Inc.
dc.rights.license.none.fl_str_mv Atribución 4.0 Internacional (CC BY 4.0)
dc.rights.uri.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
dc.rights.accessrights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Atribución 4.0 Internacional (CC BY 4.0)
© 2024 Published by Elsevier Inc.
https://creativecommons.org/licenses/by/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 19 páginas
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Academic Press Inc.
dc.publisher.place.none.fl_str_mv United states
publisher.none.fl_str_mv Academic Press Inc.
dc.source.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S0013935124014312?via%3Dihub
institution Corporación Universidad de la Costa
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/ef6128a4-de9b-4f2f-a140-5022f80e1d19/download
https://repositorio.cuc.edu.co/bitstreams/0bd0142e-b209-4fa0-89c5-a22927aaa4af/download
https://repositorio.cuc.edu.co/bitstreams/52be04eb-037d-40b5-92d4-e5294815aeb9/download
https://repositorio.cuc.edu.co/bitstreams/b469250b-0a5c-45b4-913a-f6b69848431a/download
bitstream.checksum.fl_str_mv 73a5432e0b76442b22b026844140d683
9a9c1c7d87df1316d2c94fc2a8cc6389
de6e7955643597688bf18374109b7e56
18652c329f1f75e45aff1c317dee52cb
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1849967826280906752
spelling Atribución 4.0 Internacional (CC BY 4.0)© 2024 Published by Elsevier Inc.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ximo, ChenZhaojuan, ZhangAbed, Azher M.Luning, LinHaqi, ZhangEscorcia Gutierrez, JoséShohan Ahmed, Ali A.Elimam, AliHuiting, XuHamid, AssilzadehLei, Zhen2024-10-25T22:07:45Z2024-10-25T22:07:45Z2024-07-05Ximo Chen, Zhaojuan Zhang, Azher M. Abed, Luning Lin, Haqi Zhang, José Escorcia-Gutierrez, Ahmed Ali A. Shohan, Elimam Ali, Huiting Xu, Hamid Assilzadeh, Lei Zhen, Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements, Environmental Research, Volume 260, 2024, 119526, ISSN 0013-9351, https://doi.org/10.1016/j.envres.2024.119526.0013-9351https://hdl.handle.net/11323/1353910.1016/j.envres.2024.119526Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite materials as Silver Nanoparticles (AgNPs) into RWH systems to elevate water treatment efficiency and assess the resulting environmental and energy-saving benefits. Utilizing a regression analysis approach with Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), this study will reach the study objective. In this study, the inputs are building attributes, environmental parameters, sociodemographic factors, and the algorithms SVM and KNN. At the same time, the outputs are predicted energy consumption, visual comfort outcomes, ROC-AUC values, and Kappa Indices. The integration of AgNPs into RWH systems demonstrated substantial environmental and operational benefits, achieving a 57% reduction in microbial content and 20% reductions in both chemical usage and energy consumption. These improvements highlight the potential of AgNPs to enhance water safety and reduce the environmental impact of traditional water treatments, making them a viable alternative for sustainable water management. Additionally, the use of a hybrid SVM-KNN model effectively predicted building energy usage and visual comfort, with high accuracy and precision, underscoring its utility in optimizing urban building environments for sustainability and comfort.19 páginasapplication/pdfengAcademic Press Inc.United stateshttps://www.sciencedirect.com/science/article/pii/S0013935124014312?via%3DihubDesigning energy-efficient buildings in urban centers through machine learning and enhanced clean water managementsArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Environmental ResearchAbbas, S., Mahmood, M.J., Yaseen, M., 2021. Assessing the potential for rooftop rainwater harvesting and its physio and socioeconomic impacts, Rawal watershed, Islamabad, Pakistan. Environ. Dev. Sustain. 23 (12), 17942–17963.Abbasi, T., Abbasi, S., 2011. Sources of pollution in rooftop rainwater harvesting systems and their control. Crit. Rev. Environ. Sci. Technol. 41 (23), 2097–2167.Abdipour, H., Hemati, H., 2023. Sonocatalytic process of penicillin removal usingFe2O3/effect of different parameters/degradation mechanism/kinetic study/ optimisation with response surface model. Int. J. Environ. Anal. Chem. 1–22.Abdulla, F., Abdulla, C., Eslamian, S., 2021. Concept and technology of rainwater harvesting. Handbook of Water Harvesting and Conservation. basic concepts and fundamentals, pp. 1–16.Aghamolaei, R., et al., 2018. Review of district-scale energy performance analysis: Outlooks towards holistic urban frameworks. Sustain. Cities Soc. 41, 252–264.Akbarian, M., Najafi, E., Tavakkoli-Moghaddam, R., Hosseinzadeh-Lotfi, F., 2015. A Network-Based Data Envelope Analysis Model in a Dynamic Balanced Score Card. Mathemat. Problem Eng. 2015 (1), 914108.Almeida, F.A.d., et al., 2022. Combining machine learning techniques with Kappa–Kendall indexes for robust hard-cluster assessment in substation pattern recognition. Elec. Power Syst. Res. 206, 107778.Alvarez, A.N.R., Flores-De-la-Mota, I., Anguiano, F.I.S., 2024. Smart rainwater harvesting Service design. Procedia Comput. Sci. 232, 465–472.Amasyali, K., El-Gohary, N.M., 2018. A review of data-driven building energy consumption prediction studies. Renew. Sustain. Energy Rev. 81, 1192–1205.Anagnostopoulos, T., 2021. A predictive vehicle ride sharing recommendation system for smart cities commuting. Smart Cities 4 (1), 177–191.Anayah, F.M., Almasri, M.N., 2009. Trends and occurrences of nitrate in the groundwater of the West Bank, Palestine. Appl. Geogr. 29 (4), 588–601.Anoob, F., Meera, V., 2022. Optimization of operational parameters for the treatment of roof-harvested rainwater with biologically synthesised nanosilver coated on sand. Water Supply 22 (1), 1120–1130.Anum, F., et al., 2023. Management of Botrytis Grey mold of tomato using bio-fabricated silver nanoparticles. South Afr. J. Bot. 159, 642–652.Arabnejad Khanouki, Ramli Sulong, Shariati, M., 2011. Behavior of through beam connections composed of CFSST columns and steel beams by finite element studying. Adv. Mater. Res. 168, 2329–2333.Arani, K.S., Zandi, Y., Pham, B.T., Mu’azu, M.A., Katebi, J., Mohammadhassani, M., Khorami, M., 2019. Computational optimized finite element modelling of mechanical interaction of concrete with fiber reinforced polymer. Comp. Concr. 23 (1), 61–68.Armaghani, D.J., Mirzaei, F., Shariati, M., Trung, N.T., Shariati, M., Trnavac, D., 2020. Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber. Geomech. Eng. 20 (3), 191–205.Asgari, G., Abdipour, H., Shadjou, A., 2023. A review of novel methods for Diuron removal from aqueous environments. Helyon.Behzadian, K., et al., 2018. Can smart rainwater harvesting schemes result in the improved performance of integrated urban water systems? Environ. Sci. Pollut. Control Ser. 25, 19271–19282.Bin Hariz, M., Said, D., Mouftah, H.T., 2021. A dynamic mobility Traffic model based on two modes of transport in smart cities. Smart Cities 4 (1), 253–270.Boateng, E.Y., Otoo, J., Abaye, D.A., 2020. Basic tenets of classification algorithms Knearest-neighbor, support vector machine, random forest and neural network: a review. J. Data Anal. Inf. Process. 8 (4), 341–357.Bourdeau, M., et al., 2019. Modeling and forecasting building energy consumption: a review of data-driven techniques. Sustain. Cities Soc. 48, 101533.Bulut, S., Sahin, G., 2020. Pedagojik formasyon o¨grencilerinin ˘ su tüketim davranıs¸ları ile su ayak izlerinin incelenmesi. Akdeniz Üniversitesi Egitim ˘ Fakültesi Dergisi 3 (2), 53–70.Carminati, M., et al., 2021. Miniaturized pervasive sensors for indoor health monitoring in smart cities. Smart Cities 4 (1), 146–155.Castro, M., Jara, A.J., Skarmeta, A.F., 2013. Smart lighting solutions for smart cities. In: 2013 27th International Conference on Advanced Information Networking and Applications Workshops. IEEE.Chahnasir, E.S., Zandi, Y., Shariati, M., Dehghani, E., Toghroli, A., Mohamad, E.T., Shariati, A., Safa, M., Wakil, K., Khorami, M., 2018. Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors. Smart Struct. Syst. 22 (4), 413–424.Chen, L., Ng, E., 2012. Outdoor thermal comfort and outdoor activities: a review of research in the past decade. Cities 29 (2), 118–125.Chen, J., Wang, H., Yin, W., Wang, Y., Lv, J., Wang, A., 2024. Deciphering carbon emissions in urban sewer networks: Bridging urban sewer networks with city-wide environmental dynamics. Water Res. 256, 121576. https://doi.org/10.1016/j.watres .2024.121576.Chou, J.-S., Tran, D.-S., 2018. Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders. Energy 165, 709–726.Copiaco, A., et al., 2023. An innovative deep anomaly detection of building energy consumption using energy time-series images. Eng. Appl. Artif. Intell. 119, 105775.Daghara, A., Al-Khatib, I.A., Al-Jabari, M., 2019. Quality of drinking water from springs in Palestine: west bank as a case study. Journal of Environmental and Public Health 2019.Dai, H., et al., 2024. Comparative assessment of two global sensitivity approaches considering model and parameter uncertainty. Water Resour. Res. 60 (2), e2023WR036096.Dankovich, T.A., Gray, D.G., 2011. Bactericidal paper impregnated with silver nanoparticles for point-of-use water treatment. Environ. Sci. Technol. 45 (5), 1992–1998.Dao, D.A., et al., 2021. Assessment of rainwater harvesting and maintenance practice for better drinking water quality in rural areas. J. Water Supply Res. Technol. - Aqua 70 (2), 202–216.Davoodnabi, S.M., Mirhosseini, S.M., Shariati, M., 2021. Analyzing shear strength of steel-concrete composite beam with angle connectors at elevated temperature using finite element method. Steel Compos. Struct. 40 (6), 853–868..Deb, C., et al., 2017. A review on time series forecasting techniques for building energy consumption. Renew. Sustain. Energy Rev. 74, 902–924.Ehsan Shahabi, J.J.K., Maryam Barghi, 2023. Innovative Computational Approaches to Developing Sustainable Urban Infrastructure: Optimizing Green Roof Systems for Enhanced Water Management and Environmental Benefits. Intern. J. Civil Eng. Adv. 1 (1), 20–29.Emad Toghroli, S.M., Fatemeh Moeini, Salman Maleki, 2023. Utilizing advanced machine learning algorithms for predicting the fatigue life of steel-reinforced concrete structures under variable load conditions. Intern. J. Civil Eng. Adv. 1 (1), 40–48.Farah, E., Shahrour, I., 2017. Leakage detection using smart water system: Combination of water balance and automated minimum night flow. Water Resour. Manag. 31, 4821–4833.Fei, Y., et al., 2023. Component design optimization of green roof substrate layer based on the assessment of multifunctional performance. Environ. Res. 238, 117190.Ganaie, M.A., Tanveer, M., 2022. KNN weighted reduced universum twin SVM for class imbalance learning. Knowl. Base Syst. 245, 108578.Ganji, F., et al., 2024. Evaluation of physical and chemical characteristics of wastewater and sludge of Zahedan urban wastewater treatment plant for reuse. Heliyon 10 (2), e24845.Gbetkom, P.G., et al., 2024. Lake Tanganyika basin water storage variations from 2003–2021 for water balance and flood monitoring. Remote Sens. Appl.: Society and Environment 34, 101182.Ge, J., Wang, Y., Zhou, D., Gu, Z., Meng, X., 2024. Effects of urban vegetation on microclimate and building energy demand in winter: An evaluation using coupled simulations. Sustain Cities Soc. 102, 105199. https://doi.org/10.1016/j.scs.2024.10 5199.Georgi, B., et al., 2012. Urban Adaptation to Climate Change in Europe: Challenges and Opportunities for Cities Together with Supportive National and European Policies.GhaffarianHoseini, A., et al., 2016. State of the art of rainwater harvesting systems towards promoting green built environments: a review. Desalination Water Treat. 57 (1), 95–104.Goçer, ¨ O., ¨ Torun, A.O., ¨ Bakoviç, M., 2018. Kent dıs¸ı bir üniversite kampüsünün dıs¸ mekanlarında ˆ ısıl konfor, kullanım ve mekanˆ dizim analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 33 (3), 853–874.Halhoul Merabet, G., et al., 2021. Intelligent building control systems for thermal comfort and energy-efficiency: a systematic review of artificial intelligence-assisted techniques. Renew. Sustain. Energy Rev. 144, 110969.Hu, C., et al., 2023. Toward purifying defect feature for multilabel sewer defect classification. IEEE Trans. Instrum. Meas. 72, 1–11.Hu, C., Dong, B., Shao, H., Zhang, J., Wang, Y., 2023. Toward Purifying Defect Feature for Multilabel Sewer Defect Classification. IEEE Trans. Instr. Measur. 72, 1–11. https://doi.org/10.1109/TIM.2023.3250306.Huang, Z., et al., 2021. Integrated water resource management: rethinking the contribution of rainwater harvesting. Sustainability 13 (15), 8338.Ismail, M., Shariati, M., Awal, A.A., Chiong, C.E., Chahnasir, E.S., Porbar, A., Heydari, A., Khorami, M., 2018. Strengthening of bolted shear joints in industrialized ferrocement construction. Steel Compos. Struct. 28 (6), 681–690.Jain, P., Pradeep, T., 2005. Potential of silver nanoparticle-coated polyurethane foam as an antibacterial water filter. Biotechnol. Bioeng. 90 (1), 59–63.Javan, K., et al., 2024. A review of interconnected challenges in the water–energy–food nexus: urban pollution perspective towards sustainable development. Sci. Total Environ. 912, 169319.Judeh, T., Bian, H., Shahrour, I., 2021a. GIS-based spatiotemporal mapping of groundwater potability and palatability indices in arid and semi-arid areas. Water 13 (9), 1323.Judeh, T., Shahrour, I., 2021b. Rainwater harvesting to address Current and forecasted Domestic water scarcity: application to arid and semi-arid areas. Water 13 (24), 3583.Kamani, H., et al., 2023a. N-doped TiO2 nano particles for ultra violet photocatalytic degradation of coliform and fecal coliform from hospital wastewater effluent. Global NEST Journal 25 (2), 81–88.Kamani, H., et al., 2023b. Synthesis of N-doped TiO2 nanoparticle and its application for disinfection of a treatment plant effluent from hospital wastewater. Desalination Water Treat. 289, 155–162.Kamani, H., et al., 2024. Degradation of reactive red 198 dye from aqueous solutions by combined technology advanced sonofenton with zero valent iron: characteristics/ effect of parameters/kinetic studies. Heliyon 10 (1), e23667.Kassani, S.H., Kassani, P.H., Najafi, S.E., 2018. Introducing a hybrid model of DEA and data mining in evaluating efficiency. Case Stud. Bank Bran arXiv preprint arXiv: 1810.05524.Katebi, J., Shoaei-parchin, M., Shariati, M., Trung, N.T., Khorami, M., 2020. Developed comparative analysis of metaheuristic optimization algorithms for optimal active control of structures. Eng. Comput. 36, 1539–1558.Kraus, B., et al., 2023. Improvement, digitalization and validation of a development method for enabling the utilization of sensory functions in design elements. Procedia CIRP 119, 272–277.Krishna, H.J., et al., 2005. The Texas Manual on Rainwater Harvesting, third ed. Texas Water Development Board, Austin, Texas, United States of America.Kyriazis, D., et al., 2013. Sustainable smart city IoT applications: heat and electricity management & Eco-conscious cruise control for public transportation. In: 2013 IEEE 14th International Symposium on" A World of Wireless, Mobile and Multimedia Networks"(WoWMoM). IEEE.Lauerwald, R., et al., 2023. Impact of a large-scale replacement of maize by soybean on water deficit in Europe. Agric. For. Meteorol. 343, 109781.Li, G., et al., 2018. Urban heat island effect of a typical valley city in China: responds to the global warming and rapid urbanization. Sustain. Cities Soc. 38, 736–745.Li, Z., et al., 2023. Kinetic mechanisms of methane hydrate replacement and carbon dioxide hydrate reorganization. Chem. Eng. J. 477, 146973.Lin, T.-P., Matzarakis, A., Hwang, R.-L., 2010. Shading effect on long-term outdoor thermal comfort. Build. Environ. 45 (1), 213–221.Lin, X., et al., 2023. Stable precipitation isotope records of cold wave events in Eurasia. Atmos. Res. 296, 107070.Liu, Z., et al., 2019. Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction. Energy Explor. Exploit. 37 (4), 1426–1451.Liu, Z., et al., 2024. Calculation of carbon emissions in wastewater treatment and its neutralization measures: a review. Sci. Total Environ. 912, 169356.Lu, S., et al., 2024. Influence of atmospheric circulation on the stable isotope of precipitation in the monsoon margin region. Atmos. Res. 298, 107131.Luo, M., et al., 2020. Comparing machine learning algorithms in predicting thermal sensation using ASHRAE Comfort Database II. Energy Build. 210, 109776.Luo, Z., Sinaei, H., Ibrahim, Z., Shariati, M., Jumaat, Z., Wakil, K., Khorami, M., 2019. Computational and experimental analysis of beam to column joints reinforced with CFRP plates. Steel Compos. Struct. 30 (3), 271–280.Luo, J., Zhuo, W., Xu, B., 2023. A deep neural network-based assistive decision method for financial risk prediction in carbon trading market. J. Circ. Syst. Comput., 2450153Luo, J., et al., 2024. The optimization of carbon emission prediction in low carbon energy economy under big data. IEEE Access.Ma, M., et al., 2023. Comparative analysis on international construction and demolition waste management policies and laws for policy makers in China. J. Civ. Eng. Manag. 29 (2), 107–130.Mahmoud, A.H.A., 2011. Analysis of the microclimatic and human comfort conditions in an urban park in hot and arid regions. Build. Environ. 46 (12), 2641–2656.Makselon, J., et al., 2018. Role of rain intensity and soil colloids in the retention of surfactant-stabilized silver nanoparticles in soil. Environ. Pollut. 238, 1027–1034.Martins, F., et al., 2021. A review of energy modeling tools for energy efficiency in smart cities. Smart Cities 4 (4), 1420–1436.Mashhadi, N., et al., 2021. Use of machine learning for leak detection and localization in water distribution systems. Smart Cities 4 (4), 1293–1315.McCarthy, P., 2024. Predicting trips to health care facilities: a binary logit and receiver operating characteristics (ROC) approach. Res. Transport. Econ. 103, 101411.Melo, A.F.S., et al., 2024. Novel IEC 61850-based off-site engineering and validation methodology for protection, automation, and control systems. Elec. Power Syst. Res. 232, 110409.Melville-Shreeve, P., Ward, S., Butler, D., 2016. Rainwater harvesting typologies for UK houses: a multi criteria analysis of system configurations. Water 8 (4), 129.Mikelonis, A.M., Lawler, D.F., Passalacqua, P., 2016a. Multilevel modeling of retention and disinfection efficacy of silver nanoparticles on ceramic water filters. Sci. Total Environ. 566–567, 368–377.Mikelonis, A.M., Lawler, D.F., Passalacqua, P., 2016b. Multilevel modeling of retention and disinfection efficacy of silver nanoparticles on ceramic water filters. Sci. Total Environ. 566, 368–377.Mirzaei, P.A., 2015. Recent challenges in modeling of urban heat island. Sustain. Cities Soc. 19, 200–206.Mittelman, A.M., et al., 2015. Silver dissolution and release from ceramic water filters. Environ. Sci. Technol. 49 (14), 8515–8522.Mohammad, M.T.F., 2023. Advanced computational techniques for the assessment of wind load impact on high-rise building structures. Intern. J. Civil Eng. Adv. 1 (1), 49–57.Mohammadhassani, M., Nezamabadi-Pour, H., Suhatril, M., Shariati, M., 2013. Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams. Struct. Eng. Mech. 46 (6), 853–868.Mohammadhassani, M., Nezamabadi-Pour, H., Suhatril, M., Shariati, M., 2014. An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups. Smart Struct. Syst. Int. J. 14 (5), 785–809.Mohammadhassani, M., Suhatril, M., Shariati, M., Ghanbari, F., 2013. Ductility and strength assessment of HSC beams with varying of tensile reinforcement ratios. Struct. Eng. Mech. 48 (6), 833–848.Momeni, E., Lotfi, F.H., Saen, R.F., Najafi, E., 2019. Centralized DEA-based reallocation of emission permits under cap and trade regulation. J. Clean Prod. 234, 306–314.Moradzadeh, A., et al., 2021. Heating and cooling loads forecasting for residential buildings based on hybrid machine learning applications: a comprehensive review and comparative analysis. IEEE Access 10, 2196–2215.Morteza Shariati, M.H., Asma Mohammadi Pour, 2023. Evaluating the use of recycled glass in concrete mixtures: A comprehensive strength and durability analysis using neural networks for mix ratio optimization. Intern. J. Civil Eng. Adv. 1 (1), 30–39.Mosavi, A., Bahmani, A., 2019. Energy consumption prediction using machine learning; a review.Mudumbe, M.J., Abu-Mahfouz, A.M., 2015. Smart water meter system for user-centric consumption measurement. In: 2015 IEEE 13th International Conference on Industrial Informatics (INDIN). IEEE.Naghipour, M., Niak, K.M., Shariati, M., Toghroli, A., 2020. Effect of progressive shear punch of a foundation on a reinforced concrete building behavior. Steel Compos. Struct. 35 (2), 279–294.Naveen Kumar, Kattimani, S., Marques, F.D., Nguyen-Thoi, T., Shariati, M., 2023. Geometrically nonlinear study of functionally graded saturated porous plates based on refined shear deformation plate theory and biot’s theory. Intern. J. Struct. Stab. Dyn. 23 (02), 2350013.Nawaz, M., et al., 2012. Silver disinfection of Pseudomonas aeruginosa and E. coli in rooftop harvested rainwater for potable purposes. Sci. Total Environ. 431, 20–25.Niu, Y.-L., et al., 2024. Global climate change: effects of future temperatures on emergency department visits for mental disorders in Beijing, China. Environ. Res., 119044Ozeren, ¨ O., ¨ Kayili, M.T., 2021. Designing public squares to optimize human outdoor thermal comfort: a case study in Safranbolu. Journal of Awareness 6 (1), 13–20.Palermo, S.A., Talarico, V.C., Pirouz, B., 2020. Optimizing rainwater harvesting systems for non-potable water uses and surface runoff mitigation. In: Numerical Computations: Theory and Algorithms: Third International Conference, NUMTA 2019, Crotone, Italy, June 15–21, 2019, Revised Selected Papers, Part I 3. Springer.Pan, J., Deng, Y., Yang, Y., Zhang, Y., 2023. Location-allocation modelling for rational health planning: Applying a two-step optimization approach to evaluate the spatial accessibility improvement of newly added tertiary hospitals in a metropolitan city of China. Soc. Sci. Med. 338, 116296. https://doi.org/10.1016/j. socscimed.2023.116296.Pasika, S., Gandla, S.T., 2020. Smart water quality monitoring system with cost-effective using IoT. Heliyon 6 (7), e04096.PCBS, 2018. Quantity of Water Supply for Domestic Sector, Water Consumed and Daily Consumption Per Capita in the West Bank by Governorate in.Peng, J., Yan, G., Zandi, Y., Agdas, A.S., Pourrostam, T., El-Arab, I.E., Khadimallah, M.A., 2022. Prediction and optimization of the flexural behavior of corroded concrete beams using adaptive neuro fuzzy inference system. Struct. 43, 200–208.Petkovi´c, B., Zandi, Y., Agdas, A.S., Nikoli´c, I., Deni´c, N., Koji´c, N., Khan, A., 2022. Adaptive neuro fuzzy evaluation of energy and non-energy material productivity impact on sustainable development based on circular economy and gross domestic product. Business Strat. Environ. 31 (1), 129–144.Petrolo, R., Loscri, V., Mitton, N., 2017. Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms. Transactions on emerging telecommunications technologies 28 (1), e2931.Plumbers, M., Mechanical Services Association of Australia, 2008. Rainwater Tank Design and Installation Handbook.Prasad, A., et al., 2015. Smart water quality monitoring system. In: 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). IEEE.Preeti, P., Rahman, A., 2021. A case study on reliability, water demand and economic analysis of rainwater harvesting in Australian capital cities. Water 13 (19), 2606.Qian, Y., et al., 2024. Anaerobic oxidation of diclofenac coupled with dissimilatory iron reduction: Kinetics, mechanism, and microbial community function succession. Chem. Eng. J. 489, 151027.Ramokone, A., et al., 2021. A review on behavioural propensity for building load and energy profile development – model inadequacy and improved approach. Sustain. Energy Technol. Assessments 45, 101235.Ramos, H.M., et al., 2019. Smart water management towards future water sustainable networks. Water 12 (1), 58.Ranaee, E., et al., 2021. Feasibility of rainwater harvesting and consumption in a middle eastern semiarid urban area. Water 13 (15), 2130.Ranjan, V., et al., 2020. The Internet of Things (IOT) based smart rain water harvesting system. In: 2020 6th International Conference on Signal Processing and Communication (ICSC). IEEE.Rasekh, A., et al., 2016. Smart water networks and cyber security. American Society of Civil Engineers, 01816004.Razavian, L., Naghipour, M., Shariati, M., Safa, M., 2020. Experimental study of the behavior of composite timber columns confined with hollow rectangular steel sections under compression. Struct. Eng. Mech. 74 (1), 145–156.Richards, S., et al., 2021. Sustainable water resources through harvesting rainwater and the effectiveness of a low-cost water treatment. J. Environ. Manag. 286, 112223.Robles, T., et al., 2014. An internet of things-based model for smart water management. In: 2014 28th International Conference on Advanced Information Networking and Applications Workshops. IEEE.Rostad, N., Foti, R., Montalto, F.A., 2016. Harvesting rooftop runoff to flush toilets: Drawing conclusions from four major US cities. Resour. Conserv. Recycl. 108, 97–106.Safa, M., Maleka, A., Arjomand, M.A., Khorami, M., Shariati, M., 2019. Strain rate effects on soil-geosynthetic interaction in fine-grained soil. Geomech. Eng. 19 (6), 533–542.Safa, M., Sari, P.A., Shariati, M., Suhatril, M., Trung, N.T., Wakil, K., Khorami, M., 2020. Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes. Phys. A: Stat. Mech. Appl. 550, 124046.Safa, M., Shariati, M., Ibrahim, Z., Toghroli, A., Baharom, S.B., Nor, N.M., Petkovi´c, D., 2016. Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel Compos. Struct. 21 (3), 679–688.Samadi, M.T., et al., 2024. The utility of ultraviolet beam in advanced oxidationreduction processes: a review on the mechanism of processes and possible production free radicals. Environ. Sci. Pollut. Res. Int. 31 (5), 6628–6648.Satani, S., 2024. Nano-Material Enhanced Rainwater Harvesting Surfaces.Savi´c, D., Vamvakeridou-Lyroudia, L., Kapelan, Z., 2014. Smart meters, smart water, smart societies: the iWIDGET project. Procedia Eng. 89, 1105–1112.Sayed, A.N., Himeur, Y., Bensaali, F., 2022. Deep and transfer learning for building occupancy detection: a review and comparative analysis. Eng. Appl. Artif. Intell. 115, 105254.Sedghi, Y., Zandi, Y., Shariati, M., Ahmadi, E., Azar, V.M., Toghroli, A., Wakil, K., 2018. Application of ANFIS technique on performance of C and L shaped angle shear connectors. Smart Struct. Syst. 22 (3), 335–340.Sehgal, M., Goyal, S., Kumar, S., 2022. Comparative study on energy-efficiency for Wireless Body area network using machine learning approach. In: 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE.Shahsavari-Pour, N., Modarres, M., Tavakoli-Moghadam, R., Najafi, E., 2010. Optimizing a multi-objectives time-cost-quality trade-off problem by a new hybrid genetic algorithm. World Appl. Sci. J. 10 (3), 355–363.Shang, M., Luo, J., 2021. The tapio decoupling principle and key strategies for changing factors of Chinese urban carbon footprint based on cloud computing. Int. J. Environ. Res. Publ. Health 18 (4), 2101.Shanmugasundharam, A., et al., 2023. Water quality index (WQI), multivariate statistical and GIS for assessment of surface water quality of Karamana river estuary, west coast of India. Total Environment Research Themes 6, 100031.Shariati, M., Afrazi, M., Kamyab, H., Rouhanifar, S., Toghroli, E., Safa, M., Afrazi, H., 2024. A state of the art review on geotechnical reinforcement with end life tires. Glob. J. Environ. Sci. Manag. 10 (1), 385–404.Shariati, M., Azar, S.M., Arjomand, M.A., Tehrani, H.S., Daei, M., Safa, M., 2020. Evaluating the impacts of using piles and geosynthetics in reducing the settlement of fine-grained soils under static load. Geomech. Eng. 20 (2), 87–101.Shariati, M., Davoodnabi, S.M., Toghroli, A., Kong, Z., Shariati, A., 2021. Hybridization of metaheuristic algorithms with adaptive neuro-fuzzy inference system to predict load-slip behavior of angle shear connectors at elevated temperatures. Compos. Struct. 278, 114524.Shariati, M., Ghorbani, M., Naghipour, M., Alinejad, N., Toghroli, A., 2020. The effect of RBS connection on energy absorption in tall buildings with braced tube frame system. Steel Compos. Struct. 34 (3), 393–407.Shariati, M., Grayeli, M., Shariati, A., Naghipour, M., 2020. Performance of composite frame consisting of steel beams and concrete filled tubes under fire loading. Steel Compos. Struct. 36 (5), 587–602.Chicago.Shariati, M., Mafipour, M.S., Ghahremani, B., Azarhomayun, F., Ahmadi, M., Trung, N. T., Shariati, A., 2022. A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement. Eng. Comp. 1–23.Shariati, M., Mafipour, M.S., Haido, J.H., Yousif, S.T., Toghroli, A., Trung, N.T., Shariati, A., 2020. Identification of the most influencing parameters on the properties of corroded concrete beams using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Steel Compos. Struct. 34 (1), 155.Shariati, M., Raeispour, M., Naghipour, M., Kamyab, H., Memarzadeh, A., Nematzadeh, M., Toghroli, A., 2024. Flexural behavior analysis of double honeycomb steel composite encased concrete beams: An integrated experimental and finite element study. Case Stud. Constr. Mater. 20, e03299.Skvortsov, A.N., Ilyechova, E.Y., Puchkova, L.V., 2023. Chemical background of silver nanoparticles interfering with mammalian copper metabolism. J. Hazard Mater. 451, 131093.Sły´s, D., Stec, A., 2020. Centralized or decentralized rainwater harvesting systems: a case study. Resources 9 (1), 5Soni, A., Das, P.K., Yusuf, M., Ridha, S., Kamyab, H., Alam, M.A., Prakash, C., 2023. Synergy of silica sand and waste plastics as thermoplastic composites on abrasive wear characteristics under conditions of different loads and sliding speeds. Chemosphere 323, 138233.Sotiropoulou, K.F., Vavatsikos, A.P., Botsaris, P.N., 2024. A hybrid AHP-PROMETHEE II onshore wind farms multicriteria suitability analysis using kNN and SVM regression models in northeastern Greece. Renew. Energy 221, 119795.Stratigea, A., 2012. The concept of ‘smart cities’. Towards community development? Netcom. R´eseaux, communication et territoires (26–3/4), 375–388.Tamagnone, P., et al., 2020. Rainwater harvesting techniques to face water scarcity in african drylands: hydrological efficiency assessment. Water 12 (9), 2646.Tiana, S.J., 2023. Comprehensive predictive modeling of earthquake resilience in multistory buildings utilizing advanced machine learning techniques. Intern. J. Civil Eng. Adv. 1 (1), 10–19.Toghroli, A., Mohammadhassani, M., Suhatril, M., Shariati, M., Ibrahim, Z., 2014. Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel. Compos. Struct. 17 (5), 623–639.Toghroli, A., Nasirianfar, M.S., Shariati, A., Khorami, M., Paknahad, M., Ahmadi, M., Zandi, Y., 2020. Analysis of extended end plate connection equipped with SMA bolts using component method. Steel Compos. Struct. 36 (2), 213–228.Uddin, M.G., et al., 2022. A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment. Water Res. 219, 118532.Vishnu, S., et al., 2021. IoT-Enabled solid waste management in smart cities. Smart Cities 4 (3), 1004–1017.Vladimirovaa, K., Le Blanc, D., 2015. How Well Are the Links between Education and Other Sustainable Development Goals Covered in UN Flagship Reports?: A Contribution to the Study of the Science-Policy Interface on Education in the UN System (October 2015).Wehbe, R., Shahrour, I., 2021. A bim-based smart system for fire evacuation. Future Internet 13 (9), 221.Wu, Z.Y., El-Maghraby, M., Pathak, S., 2015. Applications of deep learning for smart water networks. Procedia Eng. 119, 479–485.Wei, Z., Zandi, Y., Gholizadeh, M., Selmi, A., Roco-Videla, A., Konbr, U., 2021. On the optimization of building energy, material, and economic management using soft computing. Adv. Concr. Constr. 11 (6), 455–468.Wu, K., et al., 2024. Water rights trading planning and its application in water resources management: a water-ecology-food nexus perspective. Environ. Res., 118377Xu, J., et al., 2023. Urban rainwater utilization: a review of management modes and harvesting systems. Front. Environ. Sci. 11.Yaacoub, A., Esseghir, M., Merghem-Boulahia, L., 2023. A review of different methodologies to study occupant comfort and energy consumption. Energies 16 (4), 1634.Yazdani, M., Kabirifar, K., Frimpong, B.E., Shariati, M., Mirmozaffari, M., Boskabadi, A., 2021. Improving construction and demolition waste collection service in an urban area using a simheuristic approach: A case study in Sydney, Australia. J. Clean Prod. 280, 124138.Yousef Zandi, A.S.A., Ramezani, M., 2023. Advanced Integration of IoT and Neural Networks for Real-Time Structural Health Monitoring and Assessment of Bridges. Intern. J. Civil Eng. Adv. 1 (1), 1–9.Yu, Y., et al., 2021. Producing and storing self-sustaining drinking water from rainwater for emergency response on isolated island. Sci. Total Environ. 768, 144513.Zainah, T.A.S.M.I., Shahaboddin, S.M.S.M.S., 1801. Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. J. Intellig. Manuf. 29 (8), 1793.Zandi, Y., Burnaz, O., Durmus¸, A., 2012. Determining the temperature distributions of fire exposed reinforced concrete cross-sections with different methods. Res. J. Environ. Earth Sci. 4 (8), 782–788.Zendehboudi, A., Baseer, M.A., Saidur, R., 2018. Application of support vector machine models for forecasting solar and wind energy resources: a review. J. Clean. Prod. 199, 272–285.Zhang, L., et al., 2021. A review of machine learning in building load prediction. Appl. Energy 285, 116452.Zhang, Y., et al., 2023. Fermentative iron reduction buffers acidification and promotes microbial metabolism in marine sediments. J. Environ. Chem. Eng. 11 (5), 110922.Zhang, F., Qian, H., 2024. A comprehensive review of the environmental benefits of urban green spaces. Environ. Res., 118837Zhang, R., Yin, L., Jia, J., Yin, Y., Li, C., 2019. Application of ATS-GWIFBM operator based on improved time entropy in green building projects. Adv. Civil Eng. 2019, 3519195. https://doi.org/10.1155/2019/3519195.Zhao, J., et al., 2021. IoT-based sanitizer station network: a facilities management case study on monitoring hand sanitizer dispenser usage. Smart Cities 4 (3), 979–994.Zheng, X., et al., 2023. Rainwater harvesting for agriculture development using multiinfluence factor and fuzzy overlay techniques. Environ. Res. 238, 117189.Zheng, L., Deng, Y., 2024. Advancing rainwater treatment technologies for irrigation of urban agriculture: a pathway toward innovation. Sci. Total Environ. 916, 170087.Zhou, P., Peng, R., Xu, M., Wu, V., Navarro-Alarcon, D., 2021. Path Planning With Automatic Seam Extraction Over Point Cloud Models for Robotic Arc Welding. IEEE Robot Autom. Lett. 6 (3), 5002–5009. https://doi.org/10.1109/LRA.2021.3070828.Zhu, G., et al., 2022. Evaporation, infiltration and storage of soil water in different vegetation zones in the Qilian Mountains: a stable isotope perspective. Hydrol. Earth Syst. Sci. 26 (14), 3771–3784.191119526260Rainwater harvesting (RWH)Nano-composite materialsSilver nanoparticlesMachine learning regressionEnergy efficiency in buildingsEnvironmental impact assessmentPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/ef6128a4-de9b-4f2f-a140-5022f80e1d19/download73a5432e0b76442b22b026844140d683MD51ORIGINALDesigning energy-efficient buildings in urban centers through machine.pdfDesigning energy-efficient buildings in urban centers through machine.pdfapplication/pdf5409800https://repositorio.cuc.edu.co/bitstreams/0bd0142e-b209-4fa0-89c5-a22927aaa4af/download9a9c1c7d87df1316d2c94fc2a8cc6389MD52TEXTDesigning energy-efficient buildings in urban centers through machine.pdf.txtDesigning energy-efficient buildings in urban centers through machine.pdf.txtExtracted texttext/plain100882https://repositorio.cuc.edu.co/bitstreams/52be04eb-037d-40b5-92d4-e5294815aeb9/downloadde6e7955643597688bf18374109b7e56MD53THUMBNAILDesigning energy-efficient buildings in urban centers through machine.pdf.jpgDesigning energy-efficient buildings in urban centers through machine.pdf.jpgGenerated Thumbnailimage/jpeg12595https://repositorio.cuc.edu.co/bitstreams/b469250b-0a5c-45b4-913a-f6b69848431a/download18652c329f1f75e45aff1c317dee52cbMD5411323/13539oai:repositorio.cuc.edu.co:11323/135392024-10-26 03:00:29.437https://creativecommons.org/licenses/by/4.0/© 2024 Published by Elsevier Inc.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares