Deep Learning and Time Series for the Prediction of Monthly Precipitation. A Case Study in the Department of Boyacá, Colombia

Context: This paper proposes a method for the prediction of monthly precipitation in the department of Boyacá using models based on deep neural networks (DNNs). These approaches have achieved significant improvements in prediction accuracy when compared to traditional methods. Method: Data with a spatial resolution of 0.5° were extracted from CHIRPS 2.0 and subsequently preprocessed for the implementation of two approaches based on recurrent neural networks (RNNs) with long short-term memory (LSTM) and ConvLSTM architectures, aiming to provide accurate predictions of monthly precipitation in the studied region. Objectives: The goal of this time series analysis is to predict monthly precipitation and develop accurate models that can forecast future rainfall patterns based on historical data. This aids in water resource management and agricultural planning, as well as in mitigating the impacts of droughts or floods. Results: According to the results obtained, the LSTM model stands out for its robustness in terms of performance metrics, such as a lower mean squared error, a lower root mean squared error, and a coefficient of determination closer to 1. This demonstrates its higher accuracy compared to the ConvLSTM model.Conclusions: Deep learning models, especially RNNs with LSTM, are effective tools for predicting crucial climate data.