Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1313-1318, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1313-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
14 Sep 2017
INFLUENCE ANALYSIS OF WATERLOGGING BASED ON DEEP LEARNING MODEL IN WUHAN
Y. Pan1, Z. Shao1, T. Cheng1, Z. Wang2, and Z. Zhang2 1State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2School of Remote Sensing Information Engineering, Wuhan University, Wuhan 430079, China
Keywords: Deep Learning, Waterlogging Influence, Stacked Autoencoder, Analytic Hierarchy Process (AHP) 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.
Conference paper (PDF, 2173 KB)


Citation: Pan, Y., Shao, Z., Cheng, T., Wang, Z., and Zhang, Z.: INFLUENCE ANALYSIS OF WATERLOGGING BASED ON DEEP LEARNING MODEL IN WUHAN, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1313-1318, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1313-2017, 2017.

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