MONTHLY RAINFALL FORECAST IN THE MUNICIPALITY OF BARRA MANSA/RJ USING DEEP LEARNING TIME SERIES TECHNIQUES

Precipitation forecasting is essential for sectors such as water resources management and urban planning. In this study, a deep learning model was developed to predict rainfall in Brazilian cities, focusing on the municipality of Barra Mansa, Rio de Janeiro. Four neural network architectures were tested: FCN, Resnet, ResCNN and InceptionTime. Among them, FCN stood out significantly, presenting the lowest error rates and the best overall adjustment. The study highlights the ability of deep learning, especially through the FCN (Fully Convolutional Network - Segmented) architecture, to make accurate predictions and uncover hidden rainfall patterns. Such discoveries have great potential to improve rainfall forecasting systems and assist in decision-making in areas that require accurate climate information.