A Novel Approach for Smart Analysis of River Flooding Simulation Based on HEC-RAS: Crossformer Predictive Model Reconstruction

Flood forecasting is essential for managing hydrological conditions and providing early disaster warnings. While many studies integrate data with machine learning models for flood prediction, the development of predictive models is limited when data is insufficient. Combining existing data with hydrological and hydraulic models and machine learning provides an effective method for flood prediction. In this study, the simulation results of HEC-HMS and HEC-RAS were used to simplify modeling and flood prediction in the study area using the Crossformer deep learning model. The HEC-HMS and HEC-RAS models were used to simulate the existing data and generate additional flood scenarios. The Crossformer model was applied to learn from the simulation results, establishing a fast and accurate prediction model. The Crossformer model achieved an R2 score of 0.970 and an MAE of 0.043, demonstrating high predictive accuracy in engineering applications. This study will provide a theoretical basis for the creation of rapid prediction models for river flooding with small data samples.