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

Mahyat Shafapour Tehrany, Farzin Shabani, Mustafa Neamah Jebur, Haoyuan Hong, Wei Chen, Xiaoshen Xie

Research output: Contribution to journalArticlepeer-review

78 Citations (Scopus)
33 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)1538-1561
Number of pages24
JournalGeomatics, Natural Hazards and Risk
Volume8
Issue number2
DOIs
Publication statusPublished - 15 Dec 2017
Externally publishedYes

Keywords

  • ensemble modelling
  • Flood susceptibility mapping
  • frequency ratio (FR)
  • GIS
  • logistic regression (LR)
  • weight of evidence (WoE)

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