Improving the Accuracy of Flood Forecasting Based on Deep Learning Models and Error Correction Methods: A Case Study of the Dapoling Watershed of the Huaihe River Basin, China

Floods are major natural disasters that cause significant destruction and loss of life, especially in small catchments lacking proper monitoring. To address this, an efficient flood forecasting system is needed for timely responses. This study focuses on the Dapoling Watershed of the Huaihe River, where we developed a flood forecasting model combining long short-term memory (LSTM), a Bayesian optimization algorithm, and error-correction (EC) methods. Moreover, model performance was evaluated using various indicators like the Nash–Sutcliffe efficiency (NSE), mean absolute error, root mean square error (RMSE), and relative errors in flood volume, peak, and timing. The results indicate that: (1) LSTM is suitable for flood forecasting in the Dapoling Watershed, although its performance declines with increasing lead time, particularly in predicting peak discharge; (2) with the incorporation of the EC method, the forecasting model exhibits significant performance improvement, as evidenced by increased NSE across all lead times; and (3) the RF-LSTM demonstrates superior applicability for flood prediction in the Dapoling Watershed, consistently outperforming other EC methods across different lead times. This framework significantly boosts forecasting accuracy and lays a solid foundation for flood early-warning systems.