Citation

Zheng Y, Jing X, Lin Y, Shen D, Zhang Y, Yu M, Zhou Y. Water Sci. Technol. 2024; 89(11): 2894-2906.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.2166/wst.2024.174

PMID

38877620

Abstract

With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R(2) are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.

Language: en

Keywords

Cities; Models, Theoretical; Climate Change; real-time monitoring; *Floods; data-driven; Environmental Monitoring/methods; flood prediction; monitoring point optimization; urban drainage system