Version 1
: Received: 10 July 2024 / Approved: 11 July 2024 / Online: 11 July 2024 (12:29:50 CEST)
How to cite:
Niño Medina, J. S.; Suarez Barón, M. J.; Reyes Suarez, J. A. Application of Deep Learning for the Analysis of the Spatiotemporal Prediction of Monthly Total Precipitation in the Boyacá Department, Colombia. Preprints2024, 2024070974. https://doi.org/10.20944/preprints202407.0974.v1
Niño Medina, J. S.; Suarez Barón, M. J.; Reyes Suarez, J. A. Application of Deep Learning for the Analysis of the Spatiotemporal Prediction of Monthly Total Precipitation in the Boyacá Department, Colombia. Preprints 2024, 2024070974. https://doi.org/10.20944/preprints202407.0974.v1
Niño Medina, J. S.; Suarez Barón, M. J.; Reyes Suarez, J. A. Application of Deep Learning for the Analysis of the Spatiotemporal Prediction of Monthly Total Precipitation in the Boyacá Department, Colombia. Preprints2024, 2024070974. https://doi.org/10.20944/preprints202407.0974.v1
APA Style
Niño Medina, J. S., Suarez Barón, M. J., & Reyes Suarez, J. A. (2024). Application of Deep Learning for the Analysis of the Spatiotemporal Prediction of Monthly Total Precipitation in the Boyacá Department, Colombia. Preprints. https://doi.org/10.20944/preprints202407.0974.v1
Chicago/Turabian Style
Niño Medina, J. S., Marcó Javier Suarez Barón and José Antonio Reyes Suarez. 2024 "Application of Deep Learning for the Analysis of the Spatiotemporal Prediction of Monthly Total Precipitation in the Boyacá Department, Colombia" Preprints. https://doi.org/10.20944/preprints202407.0974.v1
Abstract
Global climate change primarily affects the spatiotemporal variation of physical quantities such as relative humidity, atmospheric pressure, ambient temperature, and notably, precipitation levels. Accurate precipitation predictions remain elusive, necessitating tools for detailed spatiotemporal analysis to better understand climate impacts on the environment, agriculture, and society. This study compares three learning models: ARIMA (Autoregressive Integrated Moving Average), RF-R (Random Forest Regression), and LSTM-NN (Long Short-Term Memory Neural Network), utilizing monthly precipitation data (in millimeters) from 757 locations in Boyacá, Colombia. The LSTM-NN model outperformed others, precisely replicating precipitation observations in both training and testing datasets, significantly reducing the root mean square error (RMSE) with average monthly deviations of approximately 19 mm per location. Evaluation metrics (RMSE, MAE, R², MSE) underscore the LSTM model’s robustness and accuracy in capturing precipitation patterns. Consequently, the LSTM model was chosen to predict precipitation over a 16-month period starting from August 2023, offering a reliable tool for future meteorological forecasting and planning in the region.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
