University of Oulu

Peyman Yariyan, Mohammadtaghi Avand, Rahim Ali Abbaspour, Ali Torabi Haghighi, Romulus Costache, Omid Ghorbanzadeh, Saeid Janizadeh & Thomas Blaschke (2020) Flood susceptibility mapping using an improved analytic network process with statistical models, Geomatics, Natural Hazards and Risk, 11:1, 2282-2314, DOI: 10.1080/19475705.2020.1836036

Flood susceptibility mapping using an improved analytic network process with statistical models

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Author: Yariyan, Peyman1; Avand, Mohammadtaghi2; Abbaspour, Rahim Ali3;
Organizations: 1Department of Surveying Engineering, Islamic Azad University Saghez Branch, Saghez, Iran
2Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, Iran
3School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
4Water Resources and Environmental Engineering, University of Oulu, Oulu, Finland
5Research Institute of the University of Bucharest, Bucharest, Romania
6National Institute of Hydrology and Water Management, Bucharest, Romania
7Department of Geoinformatics–Z_GIS, University of Salzburg, Salzburg, Austria
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 5.7 MB)
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Language: English
Published: Informa, 2020
Publish Date: 2021-01-07


Flooding is a natural disaster that causes considerable damage to different sectors and severely affects economic and social activities. The city of Saqqez in Iran is susceptible to flooding due to its specific environmental characteristics. Therefore, susceptibility and vulnerability mapping are essential for comprehensive management to reduce the harmful effects of flooding. The primary purpose of this study is to combine the Analytic Network Process (ANP) decision-making method and the statistical models of Frequency Ratio (FR), Evidential Belief Function (EBF), and Ordered Weight Average (OWA) for flood susceptibility mapping in Saqqez City in Kurdistan Province, Iran. The frequency ratio method was used instead of expert opinions to weight the criteria in the ANP. The ten factors influencing flood susceptibility in the study area are slope, rainfall, slope length, topographic wetness index, slope aspect, altitude, curvature, distance from river, geology, and land use/land cover. We identified 42 flood points in the area, 70% of which was used for modelling, and the remaining 30% was used to validate the models. The Receiver Operating Characteristic (ROC) curve was used to evaluate the results. The area under the curve obtained from the ROC curve indicates a superior performance of the ANP and EBF hybrid model (ANP-EBF) with 95.1% efficiency compared to the combination of ANP and FR (ANP-FR) with 91% and ANP and OWA (ANP-OWA) with 89.6% efficiency.

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Series: Geomatics, natural hazards & risk
ISSN: 1947-5705
ISSN-E: 1947-5713
ISSN-L: 1947-5705
Volume: 11
Issue: 1
Pages: 2282 - 2314
DOI: 10.1080/19475705.2020.1836036
Type of Publication: A1 Journal article – refereed
Field of Science: 218 Environmental engineering
Funding: This research was partly funded by the Open Access Funding of Austrian Science Fund (FWF) through the GISscience Doctoral College (DK W 1237-N23).
Copyright information: © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.