Study on urban waterlogging analysis techniques based on data model coupled with hydraulic simulation model

Abstract

[Objective] To solve the problem of inaccurate prediction and difficulty in real application of traditional waterlogging models due to data loss and insufficient accuracy, a method of coupling data model with hydraulic simulation model is studied. [Methods] Taking the Lianghe area in the central urban area of Jiujiang as the research area, data model for data cleaning,reconstruction and correction of boundary conditions is used to compensate for the shortcomings of measured data and provide data support for the operation of hydraulic simulation model. At the same time, hydraulic simulation models are used to calculate various possible working conditions and supplement training basic data for data mining models of waterlogging pridiction, in order to improve and verify their accuracy. [Results] The results show that after optimizing the data model, the average NSE of the hydraulic simulation result is improved, and the highest increase is about 20%. The SVM machine learning model can reflect the inundation range of the designed rainfall conditions, and the predicted maximum water depth is close to that simulated by the hydraulic simulation model. The RMSE is close to 0. 007. [Conclusion] Coupling data model with hydraulic simulation model to construct a new urban waterlogging model, data model can improve the boundary conditions for the operation of the mechanism model, and hydraulic simulation model can enhance the applicability of data model. The mutual coupling of the two models can achieve good application effects.

Keywords

data model / hydraulic simulation model / waterlogging model / machine learning / runoff / rainfall / extreme weather conditions / urban flood disasters