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Research Article
Hossein Afzalimehr
Iran University of Science and Technology
Corresponding Author
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Floods are one of the most damaging natural disasters throughout the world. The purpose of this study is to develop a reliable model for identification of flood susceptible areas. Three Multi-criteria decision-making techniques, namely Analytical Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Attributive Border Approximation Area Comparison (MABAC) methods combined with weight of evidence (WOE) were used in Mazandaran Province, Iran. MABAC method is applied to determine the flood susceptibility in this study, for the first time. At first, 160 flood locations were identified in the study area, of which 112 (70%) locations were selected randomly for modeling, and the remaining 48 (30%) locations were used for validation. Using Geographic Information System (GIS) with eight conditioning factors including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, the flood susceptibility maps were prepared. The results showed that the area under receiver operating characteristic curve (AUROC) for the test data of AHP-WOE, TOPSIS-WOE-AHP, and MABAC-WOE-AHP methods were 75.3%, 91.6%, and 86.1%, respectively, which indicate the reasonable accuracy of models. High accuracy of the proposed new model (MABAC) clarifies its applicability for preventive measures.
Keywords
Flood susceptibility map, GIS, MABAC method, Multi-criteria decision-making analysis, ROC
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This preprint is under consideration at Environmental Earth Sciences. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Hossein Afzalimehr
Iran University of Science and Technology
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 22 Mar, 2021
No community comments so far
Reviewers invited
Invitations sent on 16 Mar, 2021
Review #? received
Received 16 Mar, 2021
Editor assigned
On 02 Feb, 2021
First submitted
On 02 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Floods are one of the most damaging natural disasters throughout the world. The purpose of this study is to develop a reliable model for identification of flood susceptible areas. Three Multi-criteria decision-making techniques, namely Analytical Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Attributive Border Approximation Area Comparison (MABAC) methods combined with weight of evidence (WOE) were used in Mazandaran Province, Iran. MABAC method is applied to determine the flood susceptibility in this study, for the first time. At first, 160 flood locations were identified in the study area, of which 112 (70%) locations were selected randomly for modeling, and the remaining 48 (30%) locations were used for validation. Using Geographic Information System (GIS) with eight conditioning factors including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, the flood susceptibility maps were prepared. The results showed that the area under receiver operating characteristic curve (AUROC) for the test data of AHP-WOE, TOPSIS-WOE-AHP, and MABAC-WOE-AHP methods were 75.3%, 91.6%, and 86.1%, respectively, which indicate the reasonable accuracy of models. High accuracy of the proposed new model (MABAC) clarifies its applicability for preventive measures.
Figure 1
Figure 2
Figure 3
Figure 4
This preprint is available for download as a PDF.
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