GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Shafapour Tehrany, M, Shabani, F, Jebur, M, Hong,, Chen, W and Xie, X 2017, 'GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques', Geomatics, Natural Hazards and Risk, vol. 8, no. 2, pp. 1538-1561.


Document type: Journal Article
Collection: Journal Articles

Title GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques
Author(s) Shafapour Tehrany, M
Shabani, F
Jebur, M
Hong,
Chen, W
Xie, X
Year 2017
Journal name Geomatics, Natural Hazards and Risk
Volume number 8
Issue number 2
Start page 1538
End page 1561
Total pages 23
Publisher Taylor and Francis
Abstract The aim of this research was to evaluate the predictive performances of frequency ratio (FR), logistic regression (LR) and weight of evidence (WoE), in flood susceptibility mapping in China. In addition, the ensemble WoE and LR and ensemble FR and LR techniques were applied and used in the evaluation. The flood inventory map, consisting of 196 flood locations, was extracted from a number of sources. The flood inventory data were randomly divided into a testing data-set, allocating 70% for training, and the remaining 30% for validation. The 15 flood conditioning factors included in the spatial database were altitude, slope, aspect, geology, distance from river, distance from road, distance from fault, soil type, land use/cover, rainfall, Normalized Difference Vegetation Index, Stream Power Index, Topographic Wetness Index, Sediment Transport Index and curvature. For validation, success and prediction rate curves were developed using area under the curve (AUC) method. The results indicated that the highest prediction rate of 90.36% was achieved using the ensemble technique of WoE and LR. The standalone WoE produced the highest prediction rate among the individual methods. It can be concluded that WoE offers a more advanced method of mapping prone areas, compared with the FR and LR methods.
Subject Natural Hazards
Geospatial Information Systems
Photogrammetry and Remote Sensing
Keyword(s) ensemble modelling
Flood susceptibility mapping
frequency ratio (FR)
GIS
logistic regression (LR)
weight of evidence (WoE)
DOI - identifier 10.1080/19475705.2017.1362038
Copyright notice © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
ISSN 1947-5705
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