DOI:
https://doi.org/10.14483/23448393.21930Published:
2025-03-30Issue:
Vol. 30 No. 1 (2025): January-AprilSection:
Computational IntelligenceDeep Learning and Time Series for the Prediction of Monthly Precipitation. A Case Study in the Department of Boyacá, Colombia
Aprendizaje profundo y series temporales para la Predicción de la Precipitación Mensual. Estudio de caso: Departamento de Boyacá-Colombia
Keywords:
deep learning, neural networks, LSTM, ConvLSTM, time series (en).Keywords:
aprendizaje profundo, redes neuronales, LSTM, ConvLSTM, series temporales (es).Downloads
References
C. E. González Orozco, M. Porcel, D. F. Alzate Velásquez, and J. O. Orduz Rodríguez, "Extreme climate variability weakens a major tropical agricultural hub," Ecol. Ind., vol. 111, art. 106015, Apr. 2020. https://doi.org/10.1016/j.ecolind.2019.106015
H. Tarwani, Sh. Patel and P. Goel, " Deep learning approach for weather classification using pre-trained convolutional neural networks," Procedia Computer Science., vol. 252, no. 3, pp. 136-145, 2025. https://doi.org/10.1016/j.procs.2024.12.015
D.A. Moreno Gaona, M.E. Morales Puentes, J.E Gil Nova, and J.D Mercado Gómez, " Structure of bryophyte communities in the paramo complexes of Boyacá-Colombia," Revista de Biología Tropical, vol 71, no. 1, pp. 1-13, 2023. https://doi.org/10.15517/rev.biol.trop..v71i1.53584
J. C. Valderrama Balaguera, H. F. Castro Silva, and C. A. Dávila Carrillo, " Pronósticos de variables climatológicas mediante los modelos de punto de cambio y Holt-Winters," Mundo FESC, vol. 11(S2), pp. 337-352, 2021. https://doi.org/10.61799/2216-0388.986
F. Rezaei Aderyani, S. Jamshid Mousavi, F. Jafari, "Short-term rainfall forecasting using machine learning-based approaches of PSO-SVR, LSTM and CNN," Journal of Hydrology., vol. 614, pp. 789-805, 2022. https://doi.org/10.1016/j.jhydrol.2022.128463
E. Morales Rojas, E. A. Díaz Ortiz, L.M. García, and M.E. Milla Pino, "Pronóstico de precipitaciones mensuales: Un estudio de caso en las comunidades nativas del Perú," Revista Cientifica Pakamuros, vol. 9, no. 3, pp. 71-85, 2021. https://doi.org/10.37787/13azmg02
D. M. Herrera Posada and E. Aristizábal, "Artificial intelligence and machine learning model for the spatial and temporal prediction of drought events in the department of Magdalena, Colombia," INGE CUC, vol. 18, no. 2, pp. 249-265, 2022. https://doi.org/10.17981/ingecuc.18.2.2022.20
B. Zhao, H. Lu, S. Chen, J. Liu, and D. Wu, "Convolutional neural networks for time series classification," Journal of Systems Engineering and Electronics, vol. 28, no 1, pp. 162-169, 2017. https://doi.org/10.21629/JSEE.2017.01.18
C. Wang, G.Tang, W. Xiong, Z. Ma, S. Zhu, "Infrared precipitation estimation using convolutional neural network for FengYun satellites," Journal of Hydrology., vol. 603, pp. 1-12, 2022. https://doi.org/10.1016/j.jhydrol.2021.127113
A. Fahim, Q. Tan, M. Mazzi, Md. Sahabuddin, B. Naz and S. Ullah Bazai. Hybrid LSTM Self-Attention Mechanism Model for Forecasting the Reform of Scientific Research in Morocco,” Computational Intelligence and Neuroscience., vol. 2021, pp. 1-14, 2021. https://doi.org/10.1155/2021/6689204
T. Nan, W. Cao, Z. Wang, Y. Gao, L. Zhao, X. Sun, and J. Na, "Evaluation of shallow groundwater dynamics after water supplement in North China Plain based on attention-GRU model," J. Hydrol., vol. 625, pp. 128-145, 2023. https://doi.org/10.1016/j.jhydrol.2023.130085
CHIRPS, "data.chc.ucsb.edu," [Online]. Available: https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p05/
Y. E. D. Prieto, "Rainfall data for the department of Boyacá," Kaggle, 2023. [Online]: Available: https://www.kaggle.com/datasets/estebanduarte/rainfall-data-for-the-department-of-boyaca
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Copyright (c) 2025 Yesid Esteban Duarte, Marco Javier Suárez Barón, Oscar Javier García Cabrejo, César Augusto Jaramillo Acevedo, Carlos Augusto Meneses Escobar
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