Development of a Physics-Guided Neural Network Model for Effective Urban Flood Management

Arjun, Balakrishna Madayala; Jain, Ashu; Lohani, Bharat

doi:10.1061/(asce)he.1943-5584.0002196

Cited by 2 publications

(1 citation statement)

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“…It provides an e cient and accurate prediction approach that does not need to consider complex physical processes such as nonlinear uid motion (Kabir et al 2020;Chu et al 2020). Recently, these methods were extensively used in the prediction of urban ood and risk assessment (Youssef et al 2022; Zahura and Goodall 2022; Madayala et al 2022). However, these models are usually based on hydraulic model data or historical data and can only provide static predictions of urban ooding conditions (Wu et al 2020;Zhou et al 2021Zhou et al , 2022.…”

Section: Introductionmentioning

confidence: 99%

Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring

Zheng,

Jing,

Lin

et al. 2023

Preprint

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With the impact of global climate change and urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction on flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapidly 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 machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with the 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 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 conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 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.

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