Published

2025-01-31

Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review

Enfoques basados en redes neuronales y lógica difusa para la estimación de la precipitación: una revisión sistemática

DOI:

https://doi.org/10.15446/ing.investig.108609

Keywords:

precipitation, river basin, neural networks, fuzzy logic, machine learning, fuzzy inference systems (en)
precipitación, cuenca hidrográfica, redes neuronales, lógica difusa, aprendizaje automático, sistemas de inferencia difusa (es)

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Precipitation estimation at the river basin level is essential for watershed management, the analysis of extreme events and weather and climate dynamics, and hydrologic modeling. In recent years, new approaches and tools such as artificial intelligence techniques have been used for precipitation estimation, offering advantages over traditional methods. Two major paradigms are artificial neural networks and fuzzy logic systems, which can be used in a wide variety of configurations, including hybrid and modular models. This work presents a literature review on hybrid metaheuristic and artificial intelligence models based on signal processes, focusing on the applications of these techniques in precipitation analysis and estimation. The selection and comparison criteria used were the model type, the input and output variables, the performance metrics, and the fields of application. An increase in the number of this type of studies was identified, mainly in applications involving neural network models, which tend to get more sophisticated according to the availability and quality of training data. On the other hand, fuzzy logic models tend to hybridize with neural models. There are still challenges related to prediction performance and spatial and temporal resolution at the basin and micro-basin levels, but, overall, these paradigms are very promising for precipitation analysis.

La estimación de la precipitación a nivel de cuenca hidrográfica es esencial para la gestión de cuencas, el análisis de eventos extremos y dinámicas meteorológicas y climáticas, y el modelado hidrológico. En los últimos años se han empleado nuevos enfoques y herramientas como las técnicas de inteligencia artificial para estimar la precipitación, ofreciendo ventajas sobre los métodos tradicionales. Dos paradigmas principales son las redes neuronales artificiales y los sistemas de lógica difusa, que pueden utilizarse en una amplia variedad de configuraciones, incluyendo modelos híbridos y modulares. Este trabajo presenta una revisión de la literatura sobre modelos híbridos metaheurísticos y de inteligencia artificial basados en procesos de señales, centrándose en las aplicaciones de estas técnicas en el análisis y la estimación de la precipitación. Los criterios de selección y comparación utilizados fueron el tipo de modelo, las variables de entrada y salida, las métricas de desempeño y los campos de aplicación. Se identificó un aumento en el número de este tipo de estudios, principalmente en aplicaciones que involucran modelos de redes neuronales, los cuales tienden a volverse más sofisticados según la disponibilidad y calidad de los datos de entrenamiento. Por otro lado, los modelos de lógica difusa tienden a hibridarse con modelos neuronales. Aún existen desafíos relacionados con el desempeño de las predicciones y la resolución espacial y temporal a nivel de cuenca y microcuenca, pero, en general, estos paradigmas son muy prometedores para el análisis de la precipitación.

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Ruiz Hurtado, A. F., Vargas-Franco, V. and González-Salcedo, L. O. (2024). Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review. Ingeniería e Investigación, 44(3), e108609. https://doi.org/10.15446/ing.investig.108609

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Ruiz Hurtado, A.F., Vargas-Franco, V. and González-Salcedo, L.O. 2024. Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review. Ingeniería e Investigación. 44, 3 (Dec. 2024), e108609. DOI:https://doi.org/10.15446/ing.investig.108609.

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Ruiz Hurtado, A. F.; Vargas-Franco, V.; González-Salcedo, L. O. Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review. Ing. Inv. 2024, 44, e108609.

ABNT

RUIZ HURTADO, A. F.; VARGAS-FRANCO, V.; GONZÁLEZ-SALCEDO, L. O. Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review. Ingeniería e Investigación, [S. l.], v. 44, n. 3, p. e108609, 2024. DOI: 10.15446/ing.investig.108609. Disponível em: https://revistas.unal.edu.co/index.php/ingeinv/article/view/108609. Acesso em: 14 apr. 2025.

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Ruiz Hurtado, Andres Felipe, Viviana Vargas-Franco, and Luis Octavio González-Salcedo. 2024. “Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review”. Ingeniería E Investigación 44 (3):e108609. https://doi.org/10.15446/ing.investig.108609.

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Ruiz Hurtado, A. F., Vargas-Franco, V. and González-Salcedo, L. O. (2024) “Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review”, Ingeniería e Investigación, 44(3), p. e108609. doi: 10.15446/ing.investig.108609.

IEEE

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A. F. Ruiz Hurtado, V. Vargas-Franco, and L. O. González-Salcedo, “Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review”, Ing. Inv., vol. 44, no. 3, p. e108609, Dec. 2024.

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Ruiz Hurtado, A. F., V. Vargas-Franco, and L. O. González-Salcedo. “Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review”. Ingeniería e Investigación, vol. 44, no. 3, Dec. 2024, p. e108609, doi:10.15446/ing.investig.108609.

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Ruiz Hurtado, Andres Felipe, Viviana Vargas-Franco, and Luis Octavio González-Salcedo. “Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review”. Ingeniería e Investigación 44, no. 3 (December 1, 2024): e108609. Accessed April 14, 2025. https://revistas.unal.edu.co/index.php/ingeinv/article/view/108609.

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Ruiz Hurtado AF, Vargas-Franco V, González-Salcedo LO. Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review. Ing. Inv. [Internet]. 2024 Dec. 1 [cited 2025 Apr. 14];44(3):e108609. Available from: https://revistas.unal.edu.co/index.php/ingeinv/article/view/108609

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