Abstract
Abstract. This paper analyses a large number of factors related to the influence degree of urban waterlogging in depth, and constructs the Stack Autoencoder model to explore the relationship between the waterlogging points’ influence degree and their surrounding spatial data, which will be used to realize the comprehensive analysis in the waterlogging influence on the work and life of residents. According to the data of rainstorm waterlogging in 2016 July in Wuhan, the model is validated. The experimental results show that the model has higher accuracy than the traditional linear regression model. Based on the experimental model and waterlogging points distribution information in Wuhan over the years, the influence degree of different waterlogging points can be quantitatively described, which will be beneficial to the formulation of urban flood control measures and provide a reference for the design of city drainage pipe network.
Highlights
At present, the most of researches on urban waterlogging are mainly aimed at the problems of waterlogging reasons, model prediction, risk assessment, prevention and control decisions, etc
According to the relevant data provided by the basic geographic information database in Wuhan, this experiment selects 135 waterlogging points in 2016 July as the research objects, among which the top 100 waterlogging points are used as training samples and 35 points are used as test samples
According to the principle of analytic hierarchy process (AHP), the judgment matrix is constructed for eight hierarchical models
Summary
The most of researches on urban waterlogging are mainly aimed at the problems of waterlogging reasons, model prediction, risk assessment, prevention and control decisions, etc. The related researches in risk assessment usually establish a system or model to evaluate the threat of waterlogging, such as Simplified Urban Waterlogging Model (Quan et al, 2010), Network Information System (Rahadianto et al, 2015). The realization of these complicated models or systems often requires longer time and more funds, so they are more suitable for long-term planning in urban waterlogging disaster prevention and control work. In the urban short-term waterlogging disaster prevention and control work, the overall change of the city drainage system is not realistic. This paper makes a statistical analysis of a large number of relevant data around the waterlogging points, and combined with the principle of the Stacked Autoencoder network, to dig out a hidden deep relationship between these
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
