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DOI: 10.4230/LIPIcs.GIScience.2023.86
A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility (Short Paper)

Authors: Jing Yao, Ziqi Li, Xiaoxiang Zhang, Changjun Liu, and Liliang Ren

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
Flash floods, as a type of devastating natural disasters, can cause significant damage to infrastructure, agriculture, and people’s livelihoods. Mapping flash flood susceptibility has long been an effective measure to help with the development of flash flood risk reduction and management strategies. Recent studies have shown that machine learning (ML) techniques perform better than traditional statistical and process-based models in estimating flash flood susceptibility. However, a major limitation of standard ML models is that they ignore the local geographic context where flash floods occur. To address this limitation, we developed a local Geographically Weighted Random Forest (GWRF) model and compared its performance against other global and local statistical and ML alternatives using an empirical flash floods model of Jiangxi Province, China.
Cite as

Jing Yao, Ziqi Li, Xiaoxiang Zhang, Changjun Liu, and Liliang Ren. A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 86:1-86:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{yao_et_al:LIPIcs.GIScience.2023.86,
  author =	{Yao, Jing and Li, Ziqi and Zhang, Xiaoxiang and Liu, Changjun and Ren, Liliang},
  title =	{{A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{86:1--86:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.86},
  URN =		{urn:nbn:de:0030-drops-189815},
  doi =		{10.4230/LIPIcs.GIScience.2023.86},
  annote =	{Keywords: Machine Learning, Spatial Statistics, Flash floods, Susceptibility}
}
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