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, Liliang Ren



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Author Details

Jing Yao
  • Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, UK
Ziqi Li
  • Department of Geography, Florida State University, Tallahassee, FL, USA
Xiaoxiang Zhang
  • Department of Geographic Information Science, College of Hydrology and Water Resources, Hohai University, Nanjing, China
Changjun Liu
  • Department of Flood and Drought Disaster Reduction, China Institute of Water Resources and Hydropower Research, Beijing, China
Liliang Ren
  • State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing, China

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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)
https://doi.org/10.4230/LIPIcs.GIScience.2023.86

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine learning
Keywords
  • Machine Learning
  • Spatial Statistics
  • Flash floods
  • Susceptibility

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