Deep-Learning-Based LSTM Model for Predicting a Tidal River's Water Levels: A Case Study of the Kapuas Kecil River, Indonesia

International Conference on Data Science and Artificial Intelligence (Springer), 2023 · DOI: 10.1007/978-981-99-7969-1_8

Accurate prediction of water levels in tidal rivers is crucial for effective disaster management in coastal areas. This study uses the LSTM deep learning method to forecast the water level dynamics of the Kapuas Kecil River and determines the optimal window size for precise predictions — an optimal window of 336 hours (14 days). Using this window, the LSTM model consistently outperforms GRU and RNN models in comparative assessments.