Application of Deep Learning for the Analysis of the Spatiotemporal Prediction of Monthly Total Precipitation in the Boyacá Department, Colombia

Abstract

Global climate change primarily affects the spatiotemporal variation in 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 compared three learning models, the autoregressive integrated moving average (ARIMA), random forest regression (RF-R), and the long short-term memory neural network (LSTM-NN), using monthly precipitation data (in millimeters) from 757 locations in Boyacá, Colombia. The inputs for these models were based on satellite images obtained from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. 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) underscored 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.