Desarrollo de un sistema predictivo basado en inteligencia artificial para la prevención de distress y mortalidad en peces mediante condiciones ambientales en sistemas acuapónicos

El presente trabajo aborda el problema de la detección tardía de condiciones fisiológicas críticas como el estrés y la mortalidad en peces dentro de sistemas acuapónicos. Aunque el monitoreo en tiempo real mediante sensores permite registrar variables fisicoquímicas del agua, las limitaciones de los...

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Autores:
Fandiño Pelayo, Jorge Saul
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2025
Institución:
Universidad Autónoma de Bucaramanga - UNAB
Repositorio:
Repositorio UNAB
Idioma:
spa
OAI Identifier:
oai:repository.unab.edu.co:20.500.12749/32229
Acceso en línea:
http://hdl.handle.net/20.500.12749/32229
Palabra clave:
Aquaponics
Artificial intelligence
Distress
Neural networks
Supervised classification
Engineering
Theory of machines
Machine learning (Artificial intelligence)
Natural computing
Support vector machines
Physiology
Climatic changes
Ingeniería
Teoría de las máquinas
Aprendizaje automático (Inteligencia artificial)
Computación natural
Máquinas de vectores de soporte
Fisiología
Cambios climáticos
Acuaponía
Inteligencia artificial
Estrés fisiológico
Redes neuronales
Clasificación supervisada
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License
http://creativecommons.org/licenses/by-nc-nd/2.5/co/
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network_acronym_str UNAB2
network_name_str Repositorio UNAB
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dc.title.spa.fl_str_mv Desarrollo de un sistema predictivo basado en inteligencia artificial para la prevención de distress y mortalidad en peces mediante condiciones ambientales en sistemas acuapónicos
dc.title.translated.spa.fl_str_mv Development of a predictive system based on artificial intelligence for the prevention of distress and mortality in fish through environmental conditions in aquaponic systems
title Desarrollo de un sistema predictivo basado en inteligencia artificial para la prevención de distress y mortalidad en peces mediante condiciones ambientales en sistemas acuapónicos
spellingShingle Desarrollo de un sistema predictivo basado en inteligencia artificial para la prevención de distress y mortalidad en peces mediante condiciones ambientales en sistemas acuapónicos
Aquaponics
Artificial intelligence
Distress
Neural networks
Supervised classification
Engineering
Theory of machines
Machine learning (Artificial intelligence)
Natural computing
Support vector machines
Physiology
Climatic changes
Ingeniería
Teoría de las máquinas
Aprendizaje automático (Inteligencia artificial)
Computación natural
Máquinas de vectores de soporte
Fisiología
Cambios climáticos
Acuaponía
Inteligencia artificial
Estrés fisiológico
Redes neuronales
Clasificación supervisada
title_short Desarrollo de un sistema predictivo basado en inteligencia artificial para la prevención de distress y mortalidad en peces mediante condiciones ambientales en sistemas acuapónicos
title_full Desarrollo de un sistema predictivo basado en inteligencia artificial para la prevención de distress y mortalidad en peces mediante condiciones ambientales en sistemas acuapónicos
title_fullStr Desarrollo de un sistema predictivo basado en inteligencia artificial para la prevención de distress y mortalidad en peces mediante condiciones ambientales en sistemas acuapónicos
title_full_unstemmed Desarrollo de un sistema predictivo basado en inteligencia artificial para la prevención de distress y mortalidad en peces mediante condiciones ambientales en sistemas acuapónicos
title_sort Desarrollo de un sistema predictivo basado en inteligencia artificial para la prevención de distress y mortalidad en peces mediante condiciones ambientales en sistemas acuapónicos
dc.creator.fl_str_mv Fandiño Pelayo, Jorge Saul
dc.contributor.advisor.none.fl_str_mv Mendoza Castellanos, Luis Sebastián
Hernández Rojas, Luis Guillermo
dc.contributor.author.none.fl_str_mv Fandiño Pelayo, Jorge Saul
dc.contributor.cvlac.spa.fl_str_mv Mendoza Castellanos, Luis Sebastián [0000115302]
dc.contributor.googlescholar.spa.fl_str_mv Hernández Rojas, Luis Guillermo [es&oi=ao]
dc.contributor.orcid.spa.fl_str_mv Mendoza Castellanos, Luis Sebastián [0000-0001-8263-2551]
Hernández Rojas, Luis Guillermo [0000-0001-6080-5300]
Fandiño Pelayo, Jorge Saul [0000-0002-6742-6888]
dc.contributor.researchgate.spa.fl_str_mv Mendoza Castellanos, Luis Sebastián [luis-sebastian-mendoza-castellanos-37263849/]
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Investigación Recursos, Energía, Sostenibilidad - GIRES
dc.contributor.apolounab.spa.fl_str_mv Mendoza Castellanos, Luis Sebastián [luis-sebastián-mendoza-castellanos]
dc.subject.keywords.spa.fl_str_mv Aquaponics
Artificial intelligence
Distress
Neural networks
Supervised classification
Engineering
Theory of machines
Machine learning (Artificial intelligence)
Natural computing
Support vector machines
Physiology
Climatic changes
topic Aquaponics
Artificial intelligence
Distress
Neural networks
Supervised classification
Engineering
Theory of machines
Machine learning (Artificial intelligence)
Natural computing
Support vector machines
Physiology
Climatic changes
Ingeniería
Teoría de las máquinas
Aprendizaje automático (Inteligencia artificial)
Computación natural
Máquinas de vectores de soporte
Fisiología
Cambios climáticos
Acuaponía
Inteligencia artificial
Estrés fisiológico
Redes neuronales
Clasificación supervisada
dc.subject.lemb.spa.fl_str_mv Ingeniería
Teoría de las máquinas
Aprendizaje automático (Inteligencia artificial)
Computación natural
Máquinas de vectores de soporte
Fisiología
Cambios climáticos
dc.subject.proposal.spa.fl_str_mv Acuaponía
Inteligencia artificial
Estrés fisiológico
Redes neuronales
Clasificación supervisada
description El presente trabajo aborda el problema de la detección tardía de condiciones fisiológicas críticas como el estrés y la mortalidad en peces dentro de sistemas acuapónicos. Aunque el monitoreo en tiempo real mediante sensores permite registrar variables fisicoquímicas del agua, las limitaciones de los sistemas tradicionales impiden anticipar eventos adversos, afectando la sostenibilidad y la eficiencia productiva. El propósito de esta investigación fue diseñar, implementar y validar un sistema predictivo basado en inteligencia artificial que permitiera anticipar condiciones de estrés o mortalidad en peces, utilizando variables como el pH, la temperatura y el oxígeno disuelto, contribuyendo a la prevención de pérdidas productivas y al fortalecimiento del manejo sostenible en acuaponía tropical. Se desarrolló un enfoque experimental con datos obtenidos de un sistema acuapónico bajo condiciones controladas y estabilizadas mediante control PID. Se entrenaron y compararon diversos modelos de clasificación supervisada, incluyendo análisis discriminante lineal (LDA), máquinas de vectores de soporte (SVM), redes neuronales, redes neuronales optimizadas genéticamente (GA-FNN) y bosques aleatorios (Random Forest). La validación del desempeño se realizó mediante validación cruzada de cinco pliegues (k = 5) y pruebas de permutación de etiquetas para evaluar la robustez estadística. Los resultados muestran que varios modelos alcanzaron niveles de precisión superiores al 90 % en la clasificación de estados fisiológicos, permitiendo generar alertas tempranas con alta confiabilidad. En particular, el modelo Random Forest presentó el mejor desempeño global, con precisión cercana al 99 %, AUC ≈ 1.0 y F1-score ≈ 0.98. Se concluye que es viable desarrollar sistemas predictivos basados en inteligencia artificial capaces de anticipar estados críticos en peces, integrando datos ambientales obtenidos experimentalmente. Esta aproximación mejora la capacidad de respuesta ante fluctuaciones ambientales y constituye una herramienta robusta para el monitoreo proactivo del bienestar en sistemas acuapónicos tropicales.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-11-21T20:58:32Z
dc.date.available.none.fl_str_mv 2025-11-21T20:58:32Z
dc.date.issued.none.fl_str_mv 2025-11-13
dc.type.eng.fl_str_mv Thesis
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.local.spa.fl_str_mv Tesis
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.hasversion.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TD
format http://purl.org/coar/resource_type/c_db06
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12749/32229
dc.identifier.instname.spa.fl_str_mv instname:Universidad Autónoma de Bucaramanga - UNAB
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional UNAB
dc.identifier.repourl.spa.fl_str_mv repourl:https://repository.unab.edu.co
url http://hdl.handle.net/20.500.12749/32229
identifier_str_mv instname:Universidad Autónoma de Bucaramanga - UNAB
reponame:Repositorio Institucional UNAB
repourl:https://repository.unab.edu.co
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv [1] L. A. Ibrahim, H. Shaghaleh, G. M. El-Kassar, M. Abu-Hashim, y E. A. Elsadek, “Aquaponics: A sustainable path to food sovereignty and enhanced water use efficiency,” Water, vol. 15, no. 24, art. 4310, 2023. [En línea]. Disponible en: https://doi.org/10.3390/w15244310
[2] J. E. Rakocy, “Aquaponics–Integrating Fish and Plant Culture,” en Aquaculture Production Systems, J. Tidwell, Ed., Hoboken, NJ: Wiley, 2012, pp. 343–386. [En línea]. Disponible en: https://doi.org/10.1002/9781118250105.ch14
[3] C. Somerville, M. Cohen, E. Pantanella, A. Stankus y A. Lovatelli, Aquaponics: An integrated fish and vegetable production system, Roma: FAO, 2021. [En línea]. Disponible en: https://www.fao.org/3/i4021e/i4021e.pdf
[4] R. Sallenave, Important Water Quality Parameters in Aquaponics Systems, Circular CR-680, New Mexico State University, 2022. [En línea]. Disponible en: https://pubs.nmsu.edu/_circulars/CR680/
[5] M. Anila y O. Daramola, “Applications, technologies, and evaluation methods in smart aquaponics: A systematic literature review,” Artif. Intell. Rev., vol. 58, art. 25, 2025. [En línea]. Disponible en: https://doi.org/10.1007/s10462-024-11003-x
[6] A. P. Oliveira, I. Baltazar y J. P. Santos, “Overcoming barriers to aquaponics adoption in schools: a practical implementation guide,” Front. Sustain. Food Syst., vol. 9, art. 1553335, 2025. [En línea]. Disponible en: https://doi.org/10.3389/fsufs.2025.1553335
[7] E. Karakaya, N. Aydemir, M. Yıldırım, and A. A. Altuntaş, “Opportunities and Challenges of Aquaponics for Sustainable Food Production: A Review,” Frontiers in Sustainable Food Systems, vol. 7, p. 1157, 2023. doi: 10.3389/fsufs.2023.1157.
[8] H. Y. Yavuzcan Yildiz, L. Robaina, J. Pirhonen, E. Mente y D. Domínguez, “Fish welfare in aquaponic systems: Its relation to water quality with an emphasis on feed and faeces—a review,” Water, vol. 9, no. 1, art. 13, 2017. [En línea]. Disponible en: https://doi.org/10.3390/w9010013
[9] C. L. Kok, I. M. B. P. Kusuma, Y. Y. Koh, H. Tang y A. B. Lim, “Smart Aquaponics: An automated water quality management system for sustainable urban agriculture,” Electronics, vol. 13, no. 5, art. 820, 2024. [En línea]. Disponible en: https://doi.org/10.3390/electronics13050820
[10] J. P. Mandap, D. Sze, G. N. Reyes, S. M. G. Dumlao, R. S. J. Reyes y W. Y. D. Chung, “Aquaponics pH level, temperature, and dissolved oxygen monitoring and control system using Raspberry Pi as network backbone,” en Proceedings of TENCON 2018 – IEEE Region 10 Conference, Jeju, Corea del Sur, 2018, pp. 1381–1386. [En línea]. Disponible en: https://doi.org/10.1109/TENCON.2018.8650469
[11] A. M. Metin, A. Kasif, y C. Catal, “Temporal fusion transformer‑based prediction in aquaponics,” J. Supercomput., vol. 79, no. 17, pp. 19934–19958, 2023. [En línea]. Disponible en: https://doi.org/10.1007/s11227-023-05389-8
[12] X. Yang, S. Zhang, J. Liu, Q. Gao, S. Dong, y C. Zhou, “Deep learning for smart fish farming: Applications, opportunities and challenges,” arXiv preprint arXiv:2004.11848, 2020. [En línea]. Disponible en: https://doi.org/10.48550/arXiv.2004.11848
[13] F. Ma, Z. Fan, A. Nikolaeva y H. Bao, “Redefining aquaculture safety with artificial intelligence: Design innovations, trends, and future perspectives,” Fishes, vol. 10, art. 88, 2025. [En línea]. Disponible en: https://doi.org/10.3390/fishes10030088
[14] Th. Aung, R. A. Razak y A. R. Md Nor, “Artificial intelligence methods used in various aquaculture applications: A systematic literature review,” *J. World Aquac. Soc.*, vol. 56, no. 1, art. e13107, 2024. [En línea]. Disponible en: https://doi.org/10.1111/jwas.13107
[15] M. Flores-Iwasaki, G. A. Guadalupe, M. Pachas-Caycho, S. Chapa-Gonza, R. C. Mori-Zabarburú y J. C. Guerrero-Abad, “Internet of Things (IoT) Sensors for Water Quality Monitoring in Aquaculture Systems: A Systematic Review and Bibliometric Analysis,” AgriEngineering, vol. 7, no. 3, art. 78, 2025. [En línea]. Disponible en: https://doi.org/10.3390/agriengineering7030078
[16] K. K. Gayam, A. Jain, R. Singh, A. Gehlot y S. V. Akram, “Smart aquaponics with integration of AI and IoT for yield enhancement through real-time monitoring and decision support,” Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 10, pp. 2039–2049, 2023. [En línea]. Disponible en: https://doi.org/10.17762/ijritcc.v11i10.8887
[17] M. M. Rashid, A. Nayan, M. O. Rahman, S. A. Simi, J. Saha y M. G. Kibria, “IoT based Smart Water Quality Prediction for Biofloc Aquaculture,” *CoRR*, vol. abs/2208.08866, Jul. 2022. [En línea]. Disponible en: https://doi.org/10.48550/arXiv.2208.08866
[18] A. Khandakar, I. M. Elzein, M. Nahiduzzaman, M. Arselene Ayari, A. Ibn Ashraf, L. Korah, A. Zyoud, H. Ali y A. Badawi, “Smart aquaponics: An innovative machine learning framework for fish farming optimization,” Computers & Electrical Engineering, vol. 119, art. 109590, 2024. [En línea]. Disponible en: https://doi.org/10.1016/j.compeleceng.2024.109590
[19] S. C. M. Sundararajan, Y. B. Shankar, S. Panneer Selvam, N. Manogaran, K. Seerangan, D. Natesan y S. Selvarajan, “IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks,” *Sci. Rep.*, vol. 15, art. 84943, 2025. [En línea]. Disponible en: https://doi.org/10.1038/s41598-024-84943-7
[20] P. Chandramenon, A. Aggoun y F. Tchuenbou-Magaia, “Smart approaches to Aquaponics 4.0 with focus on water quality,” *Comput. Electron. Agric.*, vol. 225, art. 109256, 2024. [En línea]. Disponible en: https://doi.org/10.1016/j.compag.2024.109256
[21] R. G. D. Rahul, H. V. P. Harigovindan, R. A. H. Rasheed Abdul Haq y A. Bhide, “Attention‑driven LSTM and GRU deep learning techniques for precise water quality prediction in smart aquaculture,” *Aquaculture International*, vol. 32, pp. 8455–8478, 9 jul. 2024. [En línea]. Disponible en: https://doi.org/10.1007/s10499-024-01574-5
[22] S. C. M. Sundararajan, Y. B. Shankar, S. Panneer Selvam, N. Manogaran, K. Seerangan, D. Natesan et al., “IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks,” Sci. Rep., vol. 15, art. 84943, 2025. [En línea]. Disponible en: https://doi.org/10.1038/s41598-024-84943-7
[23] D. R. Gandh, K. P. Rasheed Abdul Haq, V. P. Harigovindan y A. Bhide, “LSTM and GRU based Accurate Water Quality Prediction for Smart Aquaculture,” Aquaculture International, vol. 32, pp. 8455–8478, 2024. [En línea]. Disponible en: https://doi.org/10.1007/s10499-024-01574-5
[24] J. Xu, Z. Xu, J. Kuang, C. Lin, L. Xiao, X. Huang, y Y. Zhang, “An Alternative to Laboratory Testing: Random Forest Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies,” Water, vol. 13, no. 22, art. 3262, 2021. [En línea]. Disponible en: https://doi.org/10.3390/w13223262
[25] H. Rharrhour et al., “Predictive Modeling of pH in a Small Scale Aquaponics System: Multi Layer Perceptron Regression, SVR and Random Forest Models,” Egypt. J. Aquat. Biol. Fish., vol. 29, no. 1, pp. 101–115, 2025. [En línea]. Disponible en: https://doi.org/10.21608/ejabf.2025.403145
[26] G. Faldani, E. Rossignolo, E. Signor, A. Longo, S. Faggion, L. Bargelloni, M. Comin y C. Pizzi, “Machine Learning Methods for Phenotype Prediction from High Dimensional, Low Population Aquaculture Data”, en Proc. 16th International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2025), pp. 638 646, 2025. [En línea]. Disponible en: https://doi.org/10.5220/0013248000003911
[27] C. Somerville, M. Cohen, E. Pantanella, A. Stankus, and A. Lovatelli, Small-scale aquaponic food production: Integrated fish and plant farming, FAO Fisheries and Aquaculture Technical Paper, no. 589, Food and Agriculture Organization, Rome, Italy, 2014. [En línea]. Disponible en: https://openknowledge.fao.org/server/api/core/bitstreams/2ca21047-390f-42cd-bd1d-0c2ebc9c1df2/content
[28] S. Goddek, A. Joyce, B. Kotzen, and G. M. Burnell, Eds., Aquaponics Food Production Systems: Combined Aquaculture and Hydroponic Production Technologies for the Future. Cham: Springer, 2019. https://doi.org/10.1007/978-3-030-15943-6
[29] B. Yep and Y. Zheng, “Aquaponic trends and challenges – a review,” Journal of Cleaner Production, vol. 228, pp. 1586–1599, 2019. https://doi.org/10.1016/j.jclepro.2019.04.290
[30] D. C. Love et al., “An international survey of aquaponics practitioners,” PLOS ONE, vol. 9, no. 7, e102662, 2014. https://doi.org/10.1371/journal.pone.0102662
[31] C. Mullins, B. Nerrie, and M. Beem, Principles of Small-Scale Aquaponics, Southern Regional Aquaculture Center, Fact Sheet no. 5007, Oklahoma State University Extension, 2015. [En línea]. Disponible en: https://extension.okstate.edu/fact-sheets/principles-of-small-scale-aquaponics.html
[32] B. Delaide, S. Goddek, and M. H. Jijakli, “Lettuce (Lactuca sativa L. var. sucrine) growth performance in aquaponic vs. hydroponic systems,” Water, vol. 8, no. 10, p. 467, 2016. https://doi.org/10.3390/w8100467
[33] B. Ali, A. Anushka, and A. Mishra, “Effects of dissolved oxygen concentration on freshwater fish: A review,” International Journal of Fisheries and Aquatic Studies, vol. 10, no. 4, pp. 113–127, 2022. https://doi.org/10.22271/fish.2022.v10.i4b.2693
[34] M. Krastanova, I. Sirakov, S. Ivanova Kirilova, D. Yarkov, and P. Orozova, “Aquaponic systems: biological and technological parameters,” Biotechnology & Biotechnological Equipment, vol. 36, no. 1, pp. 305–316, 2022. https://doi.org/10.1080/13102818.2022.2074892
[35] S. Wongkiew, Z. Hu, K. Chandran, J. W. Lee, and S. K. Khanal, “Nitrogen transformations in aquaponic systems: A review,” Aquacultural Engineering, vol. 76, pp. 9–19, 2017. https://doi.org/10.1016/j.aquaeng.2017.01.004
[36] H. Y. Yıldız, T. B. Samsunlu, D. Parisi, G. M. Burnell, and S. Goddek, “Fish welfare in aquaponic systems: Its relation to water quality with an emphasis on feed and faeces—a review,” Water, vol. 9, no. 1, p. 13, 2017. https://doi.org/10.3390/w9010013
[37] Y. Haruo, H. Yamamoto, M. Arakawa, and I. Naka, “Development and evaluation of environmental/growth observation sensor network system for aquaponics,” en Proc. 2020 IEEE Int. Conf. Consumer Electronics (ICCE), 2020. https://doi.org/10.1109/ICCE46568.2020.9043018
[38] K. A. Obirikorang, B. A. Gyampoh, and W. Asante, “Aquaponics for improved food security in Africa: A review,” Frontiers in Sustainable Food Systems, vol. 5, Art. no. 705549, 2021. https://doi.org/10.3389/fsufs.2021.705549
[39] L. H. David, S. M. Pinho, F. Agostinho, J. I. Costa, M. C. Portella, K. J. Keesman, et al., “Sustainability of urban aquaponics farms: An emergy point of view,” Journal of Cleaner Production, vol. 300, Art. no. 129896, 2021. https://doi.org/10.1016/j.jclepro.2021.129896
[40] Z. Schmautz, C. A. Espinal, T. H. M. Smits, E. Frossard, and R. Junge, “Nitrogen transformations across compartments of an aquaponic system,” Aquacultural Engineering, vol. 92, Art. no. 102145, 2021. https://doi.org/10.1016/j.aquaeng.2021.102145
[41] Z. Schmautz, C. A. Espinal, T. H. M. Smits, E. Frossard, R. Junge, and M. Krebs, “Microbial diversity across compartments in an aquaponic system and its connection to the nitrogen cycle,” Science of the Total Environment, vol. 852, Art. no. 158426, 2022. https://doi.org/10.1016/j.scitotenv.2022.158426
[42] H. Monsees, W. Klöas, and S. Wuertz, “Decoupled systems on trial: Eliminating bottlenecks to improve aquaponic processes,” PLOS ONE, vol. 12, no. 9, Art. no. e0183056, 2017. https://doi.org/10.1371/journal.pone.0183056
[43] D. C. Love, M. S. Uhl, and L. Genello, “Energy and water use of a small scale raft aquaponics system in Baltimore, Maryland, United States,” Aquacultural Engineering, vol. 68, pp. 19–27, 2015. https://doi.org/10.1016/j.aquaeng.2015.07.003
[44] B. Siswanto, Y. Dani, D. Morika, and B. Mardiyana, “A simple dataset of water quality on aquaponic fish ponds based on an internet of things measurement device,” Data in Brief, vol. 48, Art. no. 109248, 2023. https://doi.org/10.1016/j.dib.2023.109248
[45] Y.-X. Huang and F. G. Schmitt, “Time-dependent intrinsic correlation analysis of temperature and dissolved oxygen time series using empirical mode decomposition,” arXiv, 2014. [En línea]. Disponible en: https://arxiv.org/abs/1401.4201
[46] P. Schober, C. Boer, and L. A. Schwarte, “Correlation coefficients: Appropriate use and interpretation,” Anesthesia & Analgesia, vol. 126, no. 5, pp. 1763–1768, 2018. https://doi.org/10.1213/ANE.0000000000002864
[47] A. Ghasemi and S. Zahediasl, “Normality tests for statistical analysis: A guide for non-statisticians,” International Journal of Endocrinology and Metabolism, vol. 10, no. 2, pp. 486–489, 2012. https://doi.org/10.5812/ijem.3505
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Atribución-NoComercial-SinDerivadas 2.5 Colombia
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dc.coverage.spatial.spa.fl_str_mv Santander (Colombia)
dc.coverage.temporal.spa.fl_str_mv 2023-2024
dc.coverage.campus.spa.fl_str_mv UNAB Campus Bucaramanga
dc.publisher.grantor.spa.fl_str_mv Universidad Autónoma de Bucaramanga UNAB
dc.publisher.faculty.spa.fl_str_mv Facultad Ingeniería
dc.publisher.program.spa.fl_str_mv Doctorado en Ingeniería
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spelling Mendoza Castellanos, Luis Sebastián4cc18069-2661-4517-adc8-66dc99ac3437Hernández Rojas, Luis Guillermo1995c4ca-9668-4b9c-83c4-b2adeb7c1d89Fandiño Pelayo, Jorge Saul26e7417f-1a15-490f-896f-e02416df1265Mendoza Castellanos, Luis Sebastián [0000115302]Hernández Rojas, Luis Guillermo [es&oi=ao]Mendoza Castellanos, Luis Sebastián [0000-0001-8263-2551]Hernández Rojas, Luis Guillermo [0000-0001-6080-5300]Fandiño Pelayo, Jorge Saul [0000-0002-6742-6888]Mendoza Castellanos, Luis Sebastián [luis-sebastian-mendoza-castellanos-37263849/]Grupo de Investigación Recursos, Energía, Sostenibilidad - GIRESMendoza Castellanos, Luis Sebastián [luis-sebastián-mendoza-castellanos]Santander (Colombia)2023-2024UNAB Campus Bucaramanga2025-11-21T20:58:32Z2025-11-21T20:58:32Z2025-11-13http://hdl.handle.net/20.500.12749/32229instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coEl presente trabajo aborda el problema de la detección tardía de condiciones fisiológicas críticas como el estrés y la mortalidad en peces dentro de sistemas acuapónicos. Aunque el monitoreo en tiempo real mediante sensores permite registrar variables fisicoquímicas del agua, las limitaciones de los sistemas tradicionales impiden anticipar eventos adversos, afectando la sostenibilidad y la eficiencia productiva. El propósito de esta investigación fue diseñar, implementar y validar un sistema predictivo basado en inteligencia artificial que permitiera anticipar condiciones de estrés o mortalidad en peces, utilizando variables como el pH, la temperatura y el oxígeno disuelto, contribuyendo a la prevención de pérdidas productivas y al fortalecimiento del manejo sostenible en acuaponía tropical. Se desarrolló un enfoque experimental con datos obtenidos de un sistema acuapónico bajo condiciones controladas y estabilizadas mediante control PID. Se entrenaron y compararon diversos modelos de clasificación supervisada, incluyendo análisis discriminante lineal (LDA), máquinas de vectores de soporte (SVM), redes neuronales, redes neuronales optimizadas genéticamente (GA-FNN) y bosques aleatorios (Random Forest). La validación del desempeño se realizó mediante validación cruzada de cinco pliegues (k = 5) y pruebas de permutación de etiquetas para evaluar la robustez estadística. Los resultados muestran que varios modelos alcanzaron niveles de precisión superiores al 90 % en la clasificación de estados fisiológicos, permitiendo generar alertas tempranas con alta confiabilidad. En particular, el modelo Random Forest presentó el mejor desempeño global, con precisión cercana al 99 %, AUC ≈ 1.0 y F1-score ≈ 0.98. Se concluye que es viable desarrollar sistemas predictivos basados en inteligencia artificial capaces de anticipar estados críticos en peces, integrando datos ambientales obtenidos experimentalmente. Esta aproximación mejora la capacidad de respuesta ante fluctuaciones ambientales y constituye una herramienta robusta para el monitoreo proactivo del bienestar en sistemas acuapónicos tropicales.INTRODUCCIÓN 11 1. RELACIÓN DE ARTÍCULOS CON LOS OBJETIVOS DEL PROYECTO DE IA EN SISTEMAS ACUAPÓNICOS 15 2. OBJETIVOS DE LA TESIS 15 2.1 OBJETIVO GENERAL 15 2.2 OBJETIVOS ESPECÍFICOS 15 3. SMART WATER QUALITY REGULATION IN SUSTAINABLE AQUAPONICS USING PID CONTROL (2025) 16 4. ENGINEERING ASSESSMENT OF WATER QUALITY CORRELATIONS (2025) 19 5. AI-DRIVEN MONITORING FOR FISH WELFARE IN AQUAPONICS: A PREDICTIVE APPROACH (2025) 24 6. CONCLUSIONES Y TRABAJO FUTURO 29 REFERENCIAS 31DoctoradoThis research addresses the challenge of delayed detection of critical physiological conditions such as stress and mortality in fish within aquaponic systems. Although real-time water quality monitoring using sensors enables continuous measurement of physicochemical variables, traditional monitoring approaches lack predictive capacity to anticipate adverse events, thereby affecting system sustainability and production efficiency. The main objective of this study was to design, implement, and validate an artificial intelligence-based predictive system capable of anticipating stress or mortality conditions in fish using variables such as pH, temperature, and dissolved oxygen, thus contributing to loss prevention and the improvement of sustainable management in tropical aquaponics. An experimental approach was developed using data collected from an aquaponic system operating under controlled conditions stabilized by a PID-based automatic control system. Several supervised classification models were trained and compared, including linear discriminant analysis (LDA), support vector machines (SVM), artificial neural networks (ANN), genetically optimized neural networks (GA-FNN), and random forest models. Model performance was evaluated through fivefold cross-validation and label permutation tests to assess statistical robustness. The results showed that several models achieved accuracy levels above 90% in classifying physiological states, enabling early warnings with high reliability. In particular, the random forest model exhibited the best overall performance, with accuracy close to 99%, AUC ≈ 1.0, and F1-score ≈ 0.98. These findings demonstrate the feasibility of developing predictive systems based on artificial intelligence to anticipate critical physiological states in fish by integrating experimentally obtained environmental data. This approach enhances the system’s responsiveness to environmental fluctuations and provides a robust tool for proactive monitoring of fish welfare in tropical aquaponic systems.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Desarrollo de un sistema predictivo basado en inteligencia artificial para la prevención de distress y mortalidad en peces mediante condiciones ambientales en sistemas acuapónicosDevelopment of a predictive system based on artificial intelligence for the prevention of distress and mortality in fish through environmental conditions in aquaponic systemsThesisinfo:eu-repo/semantics/doctoralThesisTesishttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/acceptedVersionhttp://purl.org/redcol/resource_type/TDDoctorado en IngenieríaUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaDoctorado en IngenieríaDING-1502AquaponicsArtificial intelligenceDistressNeural networksSupervised classificationEngineeringTheory of machinesMachine learning (Artificial intelligence)Natural computingSupport vector machinesPhysiologyClimatic changesIngenieríaTeoría de las máquinasAprendizaje automático (Inteligencia artificial)Computación naturalMáquinas de vectores de soporteFisiologíaCambios climáticosAcuaponíaInteligencia artificialEstrés fisiológicoRedes neuronalesClasificación supervisada[1] L. A. Ibrahim, H. Shaghaleh, G. M. El-Kassar, M. Abu-Hashim, y E. A. Elsadek, “Aquaponics: A sustainable path to food sovereignty and enhanced water use efficiency,” Water, vol. 15, no. 24, art. 4310, 2023. [En línea]. Disponible en: https://doi.org/10.3390/w15244310[2] J. E. Rakocy, “Aquaponics–Integrating Fish and Plant Culture,” en Aquaculture Production Systems, J. Tidwell, Ed., Hoboken, NJ: Wiley, 2012, pp. 343–386. [En línea]. Disponible en: https://doi.org/10.1002/9781118250105.ch14[3] C. Somerville, M. Cohen, E. Pantanella, A. Stankus y A. Lovatelli, Aquaponics: An integrated fish and vegetable production system, Roma: FAO, 2021. [En línea]. Disponible en: https://www.fao.org/3/i4021e/i4021e.pdf[4] R. Sallenave, Important Water Quality Parameters in Aquaponics Systems, Circular CR-680, New Mexico State University, 2022. [En línea]. Disponible en: https://pubs.nmsu.edu/_circulars/CR680/[5] M. Anila y O. Daramola, “Applications, technologies, and evaluation methods in smart aquaponics: A systematic literature review,” Artif. Intell. Rev., vol. 58, art. 25, 2025. [En línea]. Disponible en: https://doi.org/10.1007/s10462-024-11003-x[6] A. P. Oliveira, I. Baltazar y J. P. Santos, “Overcoming barriers to aquaponics adoption in schools: a practical implementation guide,” Front. Sustain. Food Syst., vol. 9, art. 1553335, 2025. [En línea]. Disponible en: https://doi.org/10.3389/fsufs.2025.1553335[7] E. Karakaya, N. Aydemir, M. Yıldırım, and A. A. Altuntaş, “Opportunities and Challenges of Aquaponics for Sustainable Food Production: A Review,” Frontiers in Sustainable Food Systems, vol. 7, p. 1157, 2023. doi: 10.3389/fsufs.2023.1157.[8] H. Y. Yavuzcan Yildiz, L. Robaina, J. Pirhonen, E. Mente y D. Domínguez, “Fish welfare in aquaponic systems: Its relation to water quality with an emphasis on feed and faeces—a review,” Water, vol. 9, no. 1, art. 13, 2017. [En línea]. Disponible en: https://doi.org/10.3390/w9010013[9] C. L. Kok, I. M. B. P. Kusuma, Y. Y. Koh, H. Tang y A. B. Lim, “Smart Aquaponics: An automated water quality management system for sustainable urban agriculture,” Electronics, vol. 13, no. 5, art. 820, 2024. [En línea]. Disponible en: https://doi.org/10.3390/electronics13050820[10] J. P. Mandap, D. Sze, G. N. Reyes, S. M. G. Dumlao, R. S. J. Reyes y W. Y. D. Chung, “Aquaponics pH level, temperature, and dissolved oxygen monitoring and control system using Raspberry Pi as network backbone,” en Proceedings of TENCON 2018 – IEEE Region 10 Conference, Jeju, Corea del Sur, 2018, pp. 1381–1386. [En línea]. Disponible en: https://doi.org/10.1109/TENCON.2018.8650469[11] A. M. Metin, A. Kasif, y C. Catal, “Temporal fusion transformer‑based prediction in aquaponics,” J. Supercomput., vol. 79, no. 17, pp. 19934–19958, 2023. [En línea]. Disponible en: https://doi.org/10.1007/s11227-023-05389-8[12] X. Yang, S. Zhang, J. Liu, Q. Gao, S. Dong, y C. Zhou, “Deep learning for smart fish farming: Applications, opportunities and challenges,” arXiv preprint arXiv:2004.11848, 2020. [En línea]. Disponible en: https://doi.org/10.48550/arXiv.2004.11848[13] F. Ma, Z. Fan, A. Nikolaeva y H. Bao, “Redefining aquaculture safety with artificial intelligence: Design innovations, trends, and future perspectives,” Fishes, vol. 10, art. 88, 2025. [En línea]. Disponible en: https://doi.org/10.3390/fishes10030088[14] Th. Aung, R. A. Razak y A. R. Md Nor, “Artificial intelligence methods used in various aquaculture applications: A systematic literature review,” *J. World Aquac. Soc.*, vol. 56, no. 1, art. e13107, 2024. [En línea]. Disponible en: https://doi.org/10.1111/jwas.13107[15] M. Flores-Iwasaki, G. A. Guadalupe, M. Pachas-Caycho, S. Chapa-Gonza, R. C. Mori-Zabarburú y J. C. Guerrero-Abad, “Internet of Things (IoT) Sensors for Water Quality Monitoring in Aquaculture Systems: A Systematic Review and Bibliometric Analysis,” AgriEngineering, vol. 7, no. 3, art. 78, 2025. [En línea]. Disponible en: https://doi.org/10.3390/agriengineering7030078[16] K. K. Gayam, A. Jain, R. Singh, A. Gehlot y S. V. Akram, “Smart aquaponics with integration of AI and IoT for yield enhancement through real-time monitoring and decision support,” Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 10, pp. 2039–2049, 2023. [En línea]. Disponible en: https://doi.org/10.17762/ijritcc.v11i10.8887[17] M. M. Rashid, A. Nayan, M. O. Rahman, S. A. Simi, J. Saha y M. G. Kibria, “IoT based Smart Water Quality Prediction for Biofloc Aquaculture,” *CoRR*, vol. abs/2208.08866, Jul. 2022. [En línea]. Disponible en: https://doi.org/10.48550/arXiv.2208.08866[18] A. Khandakar, I. M. Elzein, M. Nahiduzzaman, M. Arselene Ayari, A. Ibn Ashraf, L. Korah, A. Zyoud, H. Ali y A. Badawi, “Smart aquaponics: An innovative machine learning framework for fish farming optimization,” Computers & Electrical Engineering, vol. 119, art. 109590, 2024. [En línea]. Disponible en: https://doi.org/10.1016/j.compeleceng.2024.109590[19] S. C. M. Sundararajan, Y. B. Shankar, S. Panneer Selvam, N. Manogaran, K. Seerangan, D. Natesan y S. Selvarajan, “IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks,” *Sci. Rep.*, vol. 15, art. 84943, 2025. [En línea]. Disponible en: https://doi.org/10.1038/s41598-024-84943-7[20] P. Chandramenon, A. Aggoun y F. Tchuenbou-Magaia, “Smart approaches to Aquaponics 4.0 with focus on water quality,” *Comput. Electron. Agric.*, vol. 225, art. 109256, 2024. [En línea]. Disponible en: https://doi.org/10.1016/j.compag.2024.109256[21] R. G. D. Rahul, H. V. P. Harigovindan, R. A. H. Rasheed Abdul Haq y A. Bhide, “Attention‑driven LSTM and GRU deep learning techniques for precise water quality prediction in smart aquaculture,” *Aquaculture International*, vol. 32, pp. 8455–8478, 9 jul. 2024. [En línea]. Disponible en: https://doi.org/10.1007/s10499-024-01574-5[22] S. C. M. Sundararajan, Y. B. Shankar, S. Panneer Selvam, N. Manogaran, K. Seerangan, D. Natesan et al., “IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks,” Sci. Rep., vol. 15, art. 84943, 2025. [En línea]. Disponible en: https://doi.org/10.1038/s41598-024-84943-7[23] D. R. Gandh, K. P. Rasheed Abdul Haq, V. P. Harigovindan y A. Bhide, “LSTM and GRU based Accurate Water Quality Prediction for Smart Aquaculture,” Aquaculture International, vol. 32, pp. 8455–8478, 2024. [En línea]. Disponible en: https://doi.org/10.1007/s10499-024-01574-5[24] J. Xu, Z. Xu, J. Kuang, C. Lin, L. Xiao, X. Huang, y Y. Zhang, “An Alternative to Laboratory Testing: Random Forest Based Water Quality Prediction Framework for Inland and Nearshore Water Bodies,” Water, vol. 13, no. 22, art. 3262, 2021. [En línea]. Disponible en: https://doi.org/10.3390/w13223262[25] H. Rharrhour et al., “Predictive Modeling of pH in a Small Scale Aquaponics System: Multi Layer Perceptron Regression, SVR and Random Forest Models,” Egypt. J. Aquat. Biol. Fish., vol. 29, no. 1, pp. 101–115, 2025. [En línea]. Disponible en: https://doi.org/10.21608/ejabf.2025.403145[26] G. Faldani, E. Rossignolo, E. Signor, A. Longo, S. Faggion, L. Bargelloni, M. Comin y C. Pizzi, “Machine Learning Methods for Phenotype Prediction from High Dimensional, Low Population Aquaculture Data”, en Proc. 16th International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2025), pp. 638 646, 2025. [En línea]. Disponible en: https://doi.org/10.5220/0013248000003911[27] C. Somerville, M. Cohen, E. Pantanella, A. Stankus, and A. Lovatelli, Small-scale aquaponic food production: Integrated fish and plant farming, FAO Fisheries and Aquaculture Technical Paper, no. 589, Food and Agriculture Organization, Rome, Italy, 2014. [En línea]. Disponible en: https://openknowledge.fao.org/server/api/core/bitstreams/2ca21047-390f-42cd-bd1d-0c2ebc9c1df2/content[28] S. Goddek, A. Joyce, B. Kotzen, and G. M. Burnell, Eds., Aquaponics Food Production Systems: Combined Aquaculture and Hydroponic Production Technologies for the Future. Cham: Springer, 2019. https://doi.org/10.1007/978-3-030-15943-6[29] B. Yep and Y. Zheng, “Aquaponic trends and challenges – a review,” Journal of Cleaner Production, vol. 228, pp. 1586–1599, 2019. https://doi.org/10.1016/j.jclepro.2019.04.290[30] D. C. Love et al., “An international survey of aquaponics practitioners,” PLOS ONE, vol. 9, no. 7, e102662, 2014. https://doi.org/10.1371/journal.pone.0102662[31] C. Mullins, B. Nerrie, and M. Beem, Principles of Small-Scale Aquaponics, Southern Regional Aquaculture Center, Fact Sheet no. 5007, Oklahoma State University Extension, 2015. [En línea]. Disponible en: https://extension.okstate.edu/fact-sheets/principles-of-small-scale-aquaponics.html[32] B. Delaide, S. Goddek, and M. H. Jijakli, “Lettuce (Lactuca sativa L. var. sucrine) growth performance in aquaponic vs. hydroponic systems,” Water, vol. 8, no. 10, p. 467, 2016. https://doi.org/10.3390/w8100467[33] B. Ali, A. Anushka, and A. Mishra, “Effects of dissolved oxygen concentration on freshwater fish: A review,” International Journal of Fisheries and Aquatic Studies, vol. 10, no. 4, pp. 113–127, 2022. https://doi.org/10.22271/fish.2022.v10.i4b.2693[34] M. Krastanova, I. Sirakov, S. Ivanova Kirilova, D. Yarkov, and P. Orozova, “Aquaponic systems: biological and technological parameters,” Biotechnology & Biotechnological Equipment, vol. 36, no. 1, pp. 305–316, 2022. https://doi.org/10.1080/13102818.2022.2074892[35] S. Wongkiew, Z. Hu, K. Chandran, J. W. Lee, and S. K. Khanal, “Nitrogen transformations in aquaponic systems: A review,” Aquacultural Engineering, vol. 76, pp. 9–19, 2017. https://doi.org/10.1016/j.aquaeng.2017.01.004[36] H. Y. Yıldız, T. B. Samsunlu, D. Parisi, G. M. Burnell, and S. Goddek, “Fish welfare in aquaponic systems: Its relation to water quality with an emphasis on feed and faeces—a review,” Water, vol. 9, no. 1, p. 13, 2017. https://doi.org/10.3390/w9010013[37] Y. Haruo, H. Yamamoto, M. Arakawa, and I. Naka, “Development and evaluation of environmental/growth observation sensor network system for aquaponics,” en Proc. 2020 IEEE Int. Conf. Consumer Electronics (ICCE), 2020. https://doi.org/10.1109/ICCE46568.2020.9043018[38] K. A. Obirikorang, B. A. Gyampoh, and W. Asante, “Aquaponics for improved food security in Africa: A review,” Frontiers in Sustainable Food Systems, vol. 5, Art. no. 705549, 2021. https://doi.org/10.3389/fsufs.2021.705549[39] L. H. David, S. M. Pinho, F. Agostinho, J. I. Costa, M. C. Portella, K. J. Keesman, et al., “Sustainability of urban aquaponics farms: An emergy point of view,” Journal of Cleaner Production, vol. 300, Art. no. 129896, 2021. https://doi.org/10.1016/j.jclepro.2021.129896[40] Z. Schmautz, C. A. Espinal, T. H. M. Smits, E. Frossard, and R. Junge, “Nitrogen transformations across compartments of an aquaponic system,” Aquacultural Engineering, vol. 92, Art. no. 102145, 2021. https://doi.org/10.1016/j.aquaeng.2021.102145[41] Z. Schmautz, C. A. Espinal, T. H. M. Smits, E. Frossard, R. Junge, and M. Krebs, “Microbial diversity across compartments in an aquaponic system and its connection to the nitrogen cycle,” Science of the Total Environment, vol. 852, Art. no. 158426, 2022. https://doi.org/10.1016/j.scitotenv.2022.158426[42] H. Monsees, W. Klöas, and S. Wuertz, “Decoupled systems on trial: Eliminating bottlenecks to improve aquaponic processes,” PLOS ONE, vol. 12, no. 9, Art. no. e0183056, 2017. https://doi.org/10.1371/journal.pone.0183056[43] D. C. Love, M. S. Uhl, and L. Genello, “Energy and water use of a small scale raft aquaponics system in Baltimore, Maryland, United States,” Aquacultural Engineering, vol. 68, pp. 19–27, 2015. https://doi.org/10.1016/j.aquaeng.2015.07.003[44] B. Siswanto, Y. Dani, D. Morika, and B. Mardiyana, “A simple dataset of water quality on aquaponic fish ponds based on an internet of things measurement device,” Data in Brief, vol. 48, Art. no. 109248, 2023. https://doi.org/10.1016/j.dib.2023.109248[45] Y.-X. Huang and F. G. Schmitt, “Time-dependent intrinsic correlation analysis of temperature and dissolved oxygen time series using empirical mode decomposition,” arXiv, 2014. [En línea]. Disponible en: https://arxiv.org/abs/1401.4201[46] P. Schober, C. Boer, and L. A. Schwarte, “Correlation coefficients: Appropriate use and interpretation,” Anesthesia & Analgesia, vol. 126, no. 5, pp. 1763–1768, 2018. https://doi.org/10.1213/ANE.0000000000002864[47] A. Ghasemi and S. Zahediasl, “Normality tests for statistical analysis: A guide for non-statisticians,” International Journal of Endocrinology and Metabolism, vol. 10, no. 2, pp. 486–489, 2012. https://doi.org/10.5812/ijem.3505https://apolo.unab.edu.co/en/persons/luis-sebasti%C3%A1n-mendoza-castellanos/ORIGINALElaboración_propuesta_Final_compendio_articulos_jorge.pdfElaboración_propuesta_Final_compendio_articulos_jorge.pdfTesisapplication/pdf1603421https://repository.unab.edu.co/bitstream/20.500.12749/32229/1/Elaboraci%c3%b3n_propuesta_Final_compendio_articulos_jorge.pdf7aa89ee05c63af792db41539f81192b3MD51open accessLicencia.pdfLicencia.pdfLicenciaapplication/pdf6955534https://repository.unab.edu.co/bitstream/20.500.12749/32229/6/Licencia.pdfd3283473dce515bdce0c82614fc9ac5aMD56metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8829https://repository.unab.edu.co/bitstream/20.500.12749/32229/5/license.txt3755c0cfdb77e29f2b9125d7a45dd316MD55open accessTHUMBNAILElaboración_propuesta_Final_compendio_articulos_jorge.pdf.jpgElaboración_propuesta_Final_compendio_articulos_jorge.pdf.jpgIM Thumbnailimage/jpeg7284https://repository.unab.edu.co/bitstream/20.500.12749/32229/7/Elaboraci%c3%b3n_propuesta_Final_compendio_articulos_jorge.pdf.jpgd62a08b9dec08ae91db1c8f36b882eddMD57open accessLicencia.pdf.jpgLicencia.pdf.jpgIM Thumbnailimage/jpeg4638https://repository.unab.edu.co/bitstream/20.500.12749/32229/8/Licencia.pdf.jpgd7d1e1f318d10af415e02cc5e236f4e3MD58metadata only access20.500.12749/32229oai:repository.unab.edu.co:20.500.12749/322292025-11-21 22:01:28.554open accessRepositorio Institucional | Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.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