Operational risk prediction of urban pipe network based on coupling mechanism model and data model

Abstract

[Objective] The frequent occurrence of global extreme climate events and the rapid pace of urbanization have resulted in increasingly severe urban waterlogging issues, leading to a significant rise in drainage pressure within urban pipe networks. It is of utmost importance to promptly assess the operational status of these pipe networks. [Methods] The flood process analysis mechanism model of the Wenchong drainage system in Huangpu District, Guangzhou, was established based on the SWMM model to determine the hydraulic capacity of pipeline sections under different rainfall scenarios. Subsequently, a data model incorporating both single-well and multi-well computing models was developed to predict the hydraulic capacity of urban pipe sections, with careful selection of the most suitable data model. Finally, an evaluation of operational risk for the urban pipe network was conducted using an improved analytic hierarchy process and comprehensive risk index method. [Results] The results show that:(1) The decision tree model based on the multi-well calculation mode has the highest accuracy, with an RMSE of 0.077 and a MAE of 0.030, and the single-well calculation time is 600,000 times faster than the SWMM model.(2) In the area where sensitive points are concentrated, social factors exert the most significant influence on the operational risk of the pipe network, with a weightage of 0.72. In contrast, in the area where sensitive points are not concentrated, economic factors play a dominant role in determining the operational risk of the pipe network, carrying a weightage of 0.68. [Conclusion] The decision tree model based on the multi-well calculation mode can meet the precision and timeliness requirements for urban flood emergency response. The risk of pipe network operation varies across different areas of the city, necessitating the proposal of improvement measures tailored to local conditions.

Keywords

mechanism model / data model / coupling / network running risk / pipe filling / climate change / urban waterlogging / rainfall