A Regionalization-Guided LSTM Model for Flood Forecasting in Data-Scarce Catchments

Flood forecasting in data-scarce catchments remains a major challenge due to limited observations and heterogeneity among basins. In this study, a regional long short-term memory model (R-LSTM) is proposed, in which runoff data are scalarised using catchment attributes, to reduce local influences and generate unified geomorphological-runoff factors for regional modeling. The proposed model is evaluated in the Jiaodong Peninsula, China, and compared with local LSTMs and regional LSTMs that incorporate catchment attributes in different ways. Results indicate that the R-LSTM consistently outperforms the benchmark models, especially in flood peak simulation. These findings demonstrate the effectiveness of the proposed regionalization strategy, providing a reference for flood forecasting in data-scarce regions.