Use of artificial intelligence for seasonal precipitation forecasting in the Minas Gerais state, Brazil

Abstract

Precipitation forecast is crucial for water resource management, agriculture, and climate disaster mitigation. This study investigates the use of artificial intelligence (AI) to predict seasonal precipitation in the state of Minas Gerais, Brazil. Utilizing neural networks trained on high temporal and spatial resolution datasets (GPCC, GPCP, MERGE-CPTEC, and CHIRPS), the research compares AI-based forecasts with numerical physics-based models. The results indicate that averaging multiple runs of each neural network model and using higher spatial resolution datasets, particularly CHIRPS, yield more accurate predictions, with lower Root Mean Square Errors (RMSE) and Relative Errors (RE). AI applications have shown promising results, offering forecasts with reduced computational processing time compared to traditional models. The study emphasizes the need to enhance computational infrastructure and explores the potential of advanced AI techniques to improve extreme climatic events forecasts, benefiting sectors such as agriculture and water resource management.