A hybrid CNN-LSTM approach for multi-step discharge forecasting with satellite data

Introduction Accurate river discharge forecasting is critical for flood mitigation and effective water resource management, particularly in data-scarce regions. Capturing both spatial rainfall variability and temporal discharge dynamics remains a challenge. Methods An innovative hybrid forecasting model was developed to predict hourly river discharges up to 24 h in advance. The proposed model combined the spatial analysis capabilities of convolutional neural networks with the temporal sequence modeling strengths of long short-term memory networks. It was designed to extract spatio-temporal patterns from satellite precipitation data while incorporating historical discharge trends to improve forecasting accuracy. The model was developed and internally validated on a 3 day lag dataset before being tested on an external test set with a relatively shorter 30min lag to simulate real-time conditions. Results The hybrid CNN-LSTM model demonstrated strong predictive performance across multiple evaluation metrics, including the correlation coefficient, Nash-Sutcliffe efficiency, root mean square error, and mean absolute error. Notably, our proposed model maintained a performance level well within acceptable error limits, even using the 24 h prediction horizon. The results confirmed the model's ability to generalize across datasets with differing temporal resolutions. Discussion The proposed framework provides a scalable and robust solution for improving short-term discharge predictions, particularly in upstream and unmeasured catchments where ground observations are limited. This approach has strong potential to support real-time water resource management and flood preparedness in data-scarce regions.