Integration of interval rough AHP and fuzzy logic for assessment of flood prone areas at the regional scale

Integration of interval rough AHP and fuzzy logic for assessment of flood prone areas at the regional scale

This study was conducted to prepare a flood susceptibility map in northwest of Hamadan Province, Iran. For this purpose, six criteria related to flood (i.e., distance to discharge channel, slope (%), elevation, soil texture and land use, topographic wet index, and check dams) were chosen. Then, base...

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Bibliographic Details
Published in:Acta geophysica Vol. 68; no. 2; pp. 477 - 493
Main Authors: Sepehri, Mehdi, Malekinezhad, Hossein, Jahanbakhshi, Farshid, Ildoromi, Ali Rerza, Chezgi, Javad, Ghorbanzadeh, Omid, Naghipour, Elham
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01-04-2020
Springer Nature B.V
Subjects:
Analytic hierarchy process
Check dams
Cluster analysis
Clustering
Criteria
Earth and Environmental Science
Earth Sciences
Elevation
Flood mapping
Floods
Fuzzy logic
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Land use
Research Article - Hydrology
Soil properties
Soil texture
Structural Geology
Texture
Vector quantization
Fuzzy logic
means
AHP
IRAHP
Flood
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Summary:This study was conducted to prepare a flood susceptibility map in northwest of Hamadan Province, Iran. For this purpose, six criteria related to flood (i.e., distance to discharge channel, slope (%), elevation, soil texture and land use, topographic wet index, and check dams) were chosen. Then, based on the role of these criteria on degree of flood susceptibility, were weighted both in the context of inter-weighting (fuzzy logic) and outer-criteria (Interval Rough Analytic Hierarchy Process). Finally, by combining these primary weights by weight overlay method in GIS, the flood susceptibility mapping was prepared in the study area. The resulted map based on K -means clustering and Silhouette function was divided into 9 clusters, whereas the lower clusters show low susceptibility to flood and vice versa. To assess the accuracy of the produced map, 102 flood observation points were overlaid on the clustered flood susceptibility map. The results showed that among these 102 flood points, 66 points are located in the clusters 8 and 9 and 3 points are located on cluster 7. These values show that the produced flood susceptibility mapping has a high accuracy.
ISSN:1895-6572
1895-7455
DOI:10.1007/s11600-019-00398-9