Deep Learning for Urban Flood Prediction: An LSTM Model Integrating Satellite Reanalysis and Historical Weather Data in Curitiba - dos Santos, Lucas Iuri; Gomes-Jr, Luiz (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2025: Anais do XL Simpósio Brasileiro de Bancos de Dados, 2025)
Urban floods pose increasing threats, causing significant human and economic losses. This research focuses on improving flood prevention using deep learning techniques together with satellite reanalysis data. We present the results of the implementation of an LSTM Neural Network with satellite reanalysis data to predict floods in the city of Curitiba, Brazil. To assess the benefits of the proposed model, we compare the results with datasets from previous related models. The results of each trained model are analyzed based on Receiver Operating Characteristic (ROC) curves. The results show the positive impact of the use of satellite data with respect to the area under the curve (AUC) metric.