A Technical Framework for Whole-Process Forecasting of Rainstorm-Induced Flash Floods Coupling Artificial Intelligence and Physical Mechanisms

Under the influence of global climate change and human activities, the frequency and intensity of extreme weather events—such as heavy precipitation and severe droughts—have increased markedly. Flood disasters triggered by intense rainfall have severely threatened lives, property, and regional socioeconomic development. To address the challenge of precise prevention and control of short‑duration rainstorm‑induced flash floods in the complex terrain of Northwest China, this study focuses on the Ningxia region, located within China’s arid‑semi‑arid transition zone. By integrating Water Internet technology, big data, and deep learning, we construct an intelligent flash flood disaster prevention and control system.

In rainfall forecasting, we have (1) developed a radar‑based precipitation retrieval model through data fusion and calibration, achieving a retrieval accuracy of R² > 0.75 and NMAE < 0.3; (2) proposed an attention‑mechanism‑driven radar echo extrapolation technique that attains over 80% accuracy for a 3‑hour lead time; and (3) built a rapid‑cycle, multi‑source data assimilation rainfall forecast model incorporating GNSS water vapor tomography.

For flood forecasting, we (1) introduced a forecasting technique that couples multi‑source rainfall predictions with a distributed hydrological model, yielding accuracy above 80%; and (2) constructed a runoff simulation model for mountainous basins by integrating radar and terrain data with adaptive pooling and attention mechanisms, achieving over 85% forecast accuracy.

In the domain of intelligent flood regulation, a real‑time operational model based on rainfall‑runoff forecasting has been developed. By combining flood forecasts with a simplified inundation model, the system enables large‑scale watershed flood analysis.