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.contributor.advisor | Mendoza Castellanos, Luis Sebastián | |
| dc.contributor.advisor | Hernández Rojas, Luis Guillermo | |
| dc.contributor.apolounab | Mendoza Castellanos, Luis Sebastián [luis-sebastián-mendoza-castellanos] | spa |
| dc.contributor.author | Fandiño Pelayo, Jorge Saul | |
| dc.contributor.cvlac | Mendoza Castellanos, Luis Sebastián [0000115302] | spa |
| dc.contributor.googlescholar | Hernández Rojas, Luis Guillermo [es&oi=ao] | spa |
| dc.contributor.orcid | Mendoza Castellanos, Luis Sebastián [0000-0001-8263-2551] | spa |
| dc.contributor.orcid | Hernández Rojas, Luis Guillermo [0000-0001-6080-5300] | spa |
| dc.contributor.orcid | Fandiño Pelayo, Jorge Saul [0000-0002-6742-6888] | spa |
| dc.contributor.researchgate | Mendoza Castellanos, Luis Sebastián [luis-sebastian-mendoza-castellanos-37263849/] | spa |
| dc.contributor.researchgroup | Grupo de Investigación Recursos, Energía, Sostenibilidad - GIRES | spa |
| dc.coverage.campus | UNAB Campus Bucaramanga | spa |
| dc.coverage.spatial | Santander (Colombia) | spa |
| dc.coverage.temporal | 2023-2024 | spa |
| dc.date.accessioned | 2025-11-21T20:58:32Z | |
| dc.date.available | 2025-11-21T20:58:32Z | |
| dc.date.issued | 2025-11-13 | |
| dc.degree.name | Doctorado en Ingeniería | spa |
| dc.description.abstract | 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. | spa |
| dc.description.abstractenglish | This 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. | spa |
| dc.description.degreelevel | Doctorado | spa |
| dc.description.learningmodality | Modalidad Presencial | spa |
| dc.description.tableofcontents | 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 31 | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.instname | instname:Universidad Autónoma de Bucaramanga - UNAB | spa |
| dc.identifier.reponame | reponame:Repositorio Institucional UNAB | spa |
| dc.identifier.repourl | repourl:https://repository.unab.edu.co | spa |
| dc.identifier.uri | http://hdl.handle.net/20.500.12749/32229 | |
| dc.language.iso | spa | spa |
| dc.publisher.faculty | Facultad Ingeniería | spa |
| dc.publisher.grantor | Universidad Autónoma de Bucaramanga UNAB | spa |
| dc.publisher.program | Doctorado en Ingeniería | spa |
| dc.publisher.programid | DING-1502 | |
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| dc.relation.uriapolo | https://apolo.unab.edu.co/en/persons/luis-sebasti%C3%A1n-mendoza-castellanos/ | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.creativecommons | Atribución-NoComercial-SinDerivadas 2.5 Colombia | * |
| dc.rights.local | Abierto (Texto Completo) | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/2.5/co/ | * |
| dc.subject.keywords | Aquaponics | spa |
| dc.subject.keywords | Artificial intelligence | spa |
| dc.subject.keywords | Distress | spa |
| dc.subject.keywords | Neural networks | spa |
| dc.subject.keywords | Supervised classification | spa |
| dc.subject.keywords | Engineering | spa |
| dc.subject.keywords | Theory of machines | spa |
| dc.subject.keywords | Machine learning (Artificial intelligence) | spa |
| dc.subject.keywords | Natural computing | spa |
| dc.subject.keywords | Support vector machines | spa |
| dc.subject.keywords | Physiology | spa |
| dc.subject.keywords | Climatic changes | spa |
| dc.subject.lemb | Ingeniería | spa |
| dc.subject.lemb | Teoría de las máquinas | spa |
| dc.subject.lemb | Aprendizaje automático (Inteligencia artificial) | spa |
| dc.subject.lemb | Computación natural | spa |
| dc.subject.lemb | Máquinas de vectores de soporte | spa |
| dc.subject.lemb | Fisiología | spa |
| dc.subject.lemb | Cambios climáticos | spa |
| dc.subject.proposal | Acuaponía | spa |
| dc.subject.proposal | Inteligencia artificial | spa |
| dc.subject.proposal | Estrés fisiológico | spa |
| dc.subject.proposal | Redes neuronales | spa |
| dc.subject.proposal | Clasificación supervisada | spa |
| dc.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 | spa |
| dc.title.translated | Development of a predictive system based on artificial intelligence for the prevention of distress and mortality in fish through environmental conditions in aquaponic systems | spa |
| dc.type | Thesis | eng |
| dc.type.coar | http://purl.org/coar/resource_type/c_db06 | |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.driver | info:eu-repo/semantics/doctoralThesis | spa |
| dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | spa |
| dc.type.local | Tesis | spa |
| dc.type.redcol | http://purl.org/redcol/resource_type/TD | spa |
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