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Uncertainty Analysis of Rainfall Spatial Interpolation in Urban Small Area

  • Jie Huang
  • Changfeng JingEmail author
  • Jiayun Fu
  • Zejun Huang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 270)

Abstract

Uncertainty analysis have attracted increasing attention of both theory and application over the last decades. Owing to the complex of surrounding, uncertainty analysis of rainfall in urban area is very little. Existing literatures on uncertainty analysis paid less attention on gauge density and rainfall intensity. Therefore, this study focuses on urban area, which a good complement to uncertainty research. In this study, gauge density was investigated with carefully selecting of gauge to covering evenly. Rainfall intensity data were extracted from one rainfall event at begin, summit and ending phases of rainfall process. Three traditional methods (Ordinary Kriging, RBF and IDW) and three machine methods (RF, ANN and SVM) were investigated for the uncertainty analysis. The result shows that (1) gauge density has important influence on the interpolation accuracy, and the higher gauge density means the higher accuracy. (2) The uncertainty is progressively stable with the increasing of rainfall intensity. (3) Geostatistic methods has better result than the IDW and RBF owing to considering spatial variability. The selected machine learning methods have good performance than traditional methods. However, the complex training processing and without spatial variability may reduce its practicability in modern flood management. Therefore, the combining of traditional methods and machine learning will be the good paradigm for spatial interpolation and uncertainty analysis.

Keywords

Rainfall Spatial interpolation Ordinary Kriging Random forest Machine learning 

Notes

Acknowledgments

The authors would like to thank the valuable comments from anonymous reviewers. This study is jointly supported by the National Natural Science Foundation of China (Grant No. 41771412), the Beijing Natural Science Foundation (Grant No. 8182015), Beijing Advanced innovation center for future urban design (Grant No. X18052, X18058, X18158) and the Zhejiang Province Research Program (Grant No. 2015C33064).

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Jie Huang
    • 1
  • Changfeng Jing
    • 2
    Email author
  • Jiayun Fu
    • 2
  • Zejun Huang
    • 1
  1. 1.School of Computer Science and TechnologyHangzhou Dianzi UniversityHangzhouChina
  2. 2.School of Geomatics and Urban Spatial InformaticsBeijing University of Civil, Engineering and ArchitectureBeijingChina

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