Regionalization Strategy Guided Long Short-Term Memory Model for Improving Flood Forecasting

Abstract

Flood forecasting in data-scarce catchments is challenging for hydrologists. To address this issue, a regional long short-term memory model (R-LSTM) is proposed. Given the diverse physical characteristics of sub-catchments, this model scalarises the runoff data based on catchment attributes including area, confluence path length, slope and minimum and maximum runoff values, thereby eliminating the local influence and producing a geomorphological-runoff factor as the model input. To assess the effectiveness of R-LSTMs for flood forecasting in data-scarce basins, the Jiaodong Peninsula in China was selected as the study area. The proposed R-LSTMs are compared with local LSTMs, regional LSTMs that do not use catchment attributes, or regional LSTMs that incorporate catchment attributes in different ways. The results show that R-LSTMs outperform the benchmarking LSTM models, especially in the simulation of flood peaks. The study indicates the potential of regionalization and the benefit of building the scalarised inputs of runoff data for regional LSTM that consider catchment attributes meticulously. The research findings can provide a reference for flood forecasting in data-scarce regions.