利用深度学习模型进行城市内涝影响分析

城市内涝是当前典型的一类城市自然灾害，影响着居民的生活质量。以城市内涝点作为研究对象，综合考虑内涝对城市居民工作和生活等方面造成的影响，筛选出与影响程度相关的21类空间数据。同时，基于深度学习原理构建栈式自编码神经网络模型，结合层次分析法获取的内涝点影响程度标签，剖析21类空间数据与内涝点对居民工作生活影响程度的关系，实现城市内涝对居民工作和生活影响的定量分析。实验表明，栈式自编码神经网络模型能准确地描述内涝点周围的系列空间数据与内涝影响程度之间的关系，可有效预测潜在内涝点对居民工作和生活的影响程度大小，可用于城市防洪排涝方案的制定和排水管网的优化设计。

Analysis of Urban Waterlogging Influence Based on Deep Learning Model

Urban waterlogging is a typical kind of urban natural disasters, which affecting the quality of residents' life.This paper takes a series of waterlogging points produced by urban rainstorm as the research objects, comprehensively considering the influence of urban waterlogging on the work and life of residents, and screens out 21 kinds of data related to the influence degree.At the same time, based on the principle of deep learning, we construct a stacked autoencoder neural network model. With the influence degree labels of urban waterlogging points obtained by analytic hierarchy process method, the relationship between the 21 types of data and the influence degree of waterlogging points is analyzed, which will be applied to the quantitative analysis of the influence of urban waterlogging points.The experimental results show that the proposed model in this paper can describe the relationship between the spatial data and the influence degree accurately. In addition, this model can effectively predict the influence degree of potential waterlogging points, which is not only beneficial to the formulation of the urban waterlogging prevention scheme, but also provides a reference for the design of urban drainage pipe network.