Long-term river flow forecasting: An integrated deep learning model with multi-scale feature extraction

River flow forecasting is crucial for water resource management, flood prevention, and environmental sustainability. River flow forecasting is crucial for water resource management, flood prevention, and environmental sustainability. Despite the application of many deep learning models in river flow prediction, they often face challenges such as limited prediction durations and insufficient accuracy. In this study, we propose an integrated deep learning model based on multi-scale feature extraction to enhance the accuracy of long-term river flow forecasts. The model integrates a multi-scale feature extraction module and a context-aware module. The former is responsible for capturing diverse features of river flow at multiple scales, while the latter further analyzes and models these features. Together, these modules enhance the model’s performance in long-term river flow forecasting. Experimental results on a river flow dataset, predicting the flow for the next 120 h, demonstrate that the proposed model maintains high accuracy, thus validating its effectiveness for long-term river flow prediction.