UTILIZING LONG SHORT-TERM MEMORY (LSTM) NETWORKS FOR RIVER FLOW PREDICTION IN THE BRAZILIAN PANTANAL BASIN

• Autor(a) principal: Sampaio Descovi, Cassiano
• Data de Publicação: 2023
• Outros Autores: Carlos Zuffo, Antonio, Mohammadizadeh, SeyedMehdi, Murillo Bermúdez, Luis Fernando, Alfonso Sierra, Daniel
• Tipo de documento: Artigo
• Idioma: por; eng
• Título da fonte: Holos
• Texto Completo: http://www2.ifrn.edu.br/ojs/index.php/HOLOS/article/view/16315
• topic: LSTM; River flows; Pantanal; LSTM, River flows, Pantanal.; LSTM, simulação de vazão, Pantanal.

This article demonstrates the successful application of Long Short-Term Memory (LSTM) recurrent neural networks to simulate streamflow in the Aquidauana River basin, located in the Brazilian Pantanal. The LSTM network used daily precipitation data as input to predict future streamflow in the region. The results obtained from this research show a coefficient of determination (R2) of 0.82, indicating a strong fit of the model to the observed data. Additionally, the Root Mean Squared Error (RMSE) was found to be 0.53, indicating the model's accuracy in predicting streamflow compared to actual data. These findings highlight the effectiveness of LSTM networks in hydrological modeling for the Pantanal region, which is crucial for water resource planning and sustainable management in this ecologically significant area. This study is expected to serve as a catalyst for further research and make a substantial contribution to the advancement of streamflow prediction techniques in complex watersheds such as the Aquidauana River basin.