Artificial neural networks applied to time series for flood prediction - Mesquita, Laleska A. F.; Ranieri, Caetano M.; Ueyama, Jó (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2023: Anais do XX Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2023.234355, 2023)
Extreme hydrological events and lack of urban planning can generate climate-related disasters. Several fields of study, including artificial intelligence, contribute to mitigate this problem and develop preventive solutions. This study focuses on flood forecasting the Xingu River using time series data. The main approach is to standardize the pure data from different stations using quantiles, and thus generate recurrence plots for the time series and then transform them into two-dimensional representations to be applied in the convolutional neural network model. The combination of recurrence plot with CNN provided data metrics in the prediction test with superior performance compared to the algorithms models implemented as LSTM, RNN.