Real‐time prediction and ponding process early warning method at urban flood points based on different deep learning methods

Abstract

Accurate prediction of urban floods is regarded as one of the critical means to prevent urban floods and reduce the losses caused by floods. In this study, a refined prediction and early warning method system for urban flood and waterlogging processes based on deep learning methods is proposed. The spatial autocorrelation of rain and ponding points is analyzed by Moran's I (a common used statistic for spatial autocorrelation). For each ponding point, the relationship model between the rainfall process and ponding process is constructed based on different deep learning methods, and the results are analyzed and verified by mean absolute error (MAE), root mean square error (RMSE), Nash efficiency coefficient (NSE) and correlation coefficient (CC). The results show that the gradient boosting decision tree algorithm has the highest accuracy and efficiency (with a 0.001 m RMSE of the predicted and measured ponding depth) for ponding process prediction and is regarded as the most suitable method for ponding process prediction. Finally, the real‐time prediction and early warning of urban floods and waterlogging processes driven by rainfall forecast data are realized, and the results are verified by the measured data. The research results can provide theoretical support for urban flood prevention and control.

A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning

“…Spatial autocorrelation analysis improves flood warnings by refining spatial data patterns for more accurate predictions. Zhou et al [114] introduced an ML-based urban flood warning system. Spatial autocorrelation analysis, using Moran's I, confirmed a significant positive spatial correlation between rainfall (at a 95% confidence level) and inundation points.…”