Applications of stacking/blending ensemble learning approaches for evaluating flash flood susceptibility

Yao, J. , Zhang, X., Lui, W., Liu, C. and Ren, L. (2022) Applications of stacking/blending ensemble learning approaches for evaluating flash flood susceptibility. International Journal of Applied Earth Observation and Geoinformation, 112, 102932. (doi: 10.1016/j.jag.2022.102932)

Abstract

Flash floods are a type of catastrophic disasters which cause significant losses of life and property worldwide. In recent years, machine learning techniques have become powerful tools for evaluating flash flood susceptibility. This research applies stacking and blending ensemble learning approaches to assess the flash flood potential in Jiangxi, China. Four base models – linear regression, K-nearest neighbours, support vector machine, and random forest – are adopted to build the two ensemble models. All models are evaluated by three metrics (accuracy, true positive rate, and the area under the receiver operating characteristic curve) and compared with a Bayesian approach. The results suggest that the blending approach is superior to all the other models, which has then been selected to evaluate the vulnerability of flash floods for all the catchments in Jiangxi. The derived maps of flash flood susceptibility suggest that over half of the province, in terms of either area or the number of catchments, are prone to flash floods, in particular the north, northeast and south. These empirical findings can help to develop plans for disaster prevention and control, as well as improving public knowledge of flash flood hazards.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Yao, Dr Jing and Zhang, Dr Xiaoxiang

Creator Roles

Yao, J.: Conceptualization, Funding acquisition, Methodology, Software, Writing – original draft, Formal analysis
Zhang, X.: Conceptualization, Funding acquisition, Methodology, Software, Writing – original draft, Formal analysis

• Authors: Yao, J., Zhang, X., Lui, W., Liu, C., and Ren, L.
• College/School: College of Social Sciences > School of Social and Political Sciences; College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: International Journal of Applied Earth Observation and Geoinformation
• Publisher: Elsevier
• ISSN: 1569-8432
• ISSN (Online): 1872-826X
• Published Online: 30 July 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in International Journal of Applied Earth Observation and Geoinformation 112: 102932
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
300882		GCRF Centre for Sustainable, Healthy, and Learning Cities and Neighbourhoods (CSHLH)	Ya Ping Wang	Economic and Social Research Council (ESRC)	ES/P011020/1	S&PS - Urban Studies
304042		UBDC Centre Transition	Nick Bailey	Economic and Social Research Council (ESRC)	ES/S007105/1	S&PS - Administration