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This research aims to enhance flood forecasting through advanced deep learning models for river level prediction.

December 8, 2025AIP AdvancesOpen Access

Application of deep learning for hourly river level forecast: A case study of Sarawak River, Malaysia

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AI models enhance prediction accuracy for flood forecasting in Sarawak River, suggesting new water management strategies.

This research aims to enhance flood forecasting through advanced deep learning models for river level prediction.
Applied four AI models: LSTM, BiLSTM, CNN-BiLSTM, and CNN-BiLSTM-XGB to analyze river data.
Focused on the Sarawak River, Malaysia for practical implementation.
Compared model performance on training, validation, and test sets.
CNN-BiLSTM-XGB model achieved an R2 value of 0.973, a 17.1% improvement over LSTM.
Demonstrated superior prediction accuracy and robustness through extensive simulations.
Other models showed overfitting on test sets with R2 values below 0.9.

H. C. Zhou (2025) studied this question.

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