Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia

Wubalem Azemeraw; Tesfaw Gashaw; Dawit Zerihun; Getahun Belete; Mekuria Tamrat; Jothimani Muralitharan

doi:10.1515/geo-2020-0329

Open Geosciences (Dec 2021)

Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia

Wubalem Azemeraw,
Tesfaw Gashaw,
Dawit Zerihun,
Getahun Belete,
Mekuria Tamrat,
Jothimani Muralitharan

Affiliations

Wubalem Azemeraw: Department of Geology, College of Natural and Computational Sciences, University of Gondar, P.O. BOX 196, Maraki Street, Ethiopia
Tesfaw Gashaw: Department of Geology, College of Natural and Computational Sciences, University of Gondar, P.O. BOX 196, Maraki Street, Ethiopia
Dawit Zerihun: Department of Geology, College of Natural and Computational Sciences, University of Gondar, P.O. BOX 196, Maraki Street, Ethiopia
Getahun Belete: Department of Geology, College of Natural and Computational Sciences, University of Gondar, P.O. BOX 196, Maraki Street, Ethiopia
Mekuria Tamrat: Department of Geology, College of Natural and Computational Sciences, University of Gondar, P.O. BOX 196, Maraki Street, Ethiopia
Jothimani Muralitharan: Department of Geology, College of Natural and Computational Sciences, University of Gondar, P.O. BOX 196, Maraki Street, Ethiopia

DOI: https://doi.org/10.1515/geo-2020-0329
Journal volume & issue: Vol. 13, no. 1
pp. 1668 – 1688

Abstract

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The flood is one of the frequently occurring natural hazards within the sub-basin of Lake Tana. The flood hazard within the sub-basin of Lake Tana causes damage to cropland, properties, and a fatality every season. Therefore, flood susceptibility modeling in this area is significant for hazard reduction and management purposes. Thus, the analytical hierarchy process (AHP), bivariate (information value [IV] and frequency ratio [FR]), and multivariate (logistic regression [LR]) statistical methods were applied. Using an intensive field survey, historical document, and Google Earth Imagery, 1,404-flood locations were determined, classified into 70% training datasets and 30% testing flood datasets using a subset within the geographic information system (GIS) environment. The statistical relationship between the probability of flood occurrence and 11 flood-driving factors was performed using the GIS tool. The flood susceptibility maps of the study area were developed by summing all weighted aspects using a raster calculator. It is classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The accuracy and performance of the models were evaluated using the area under the curve (AUC). As the result indicated, the FR model has better performance (AUC = 99.1%) compared to the AHP model (AUC = 86.9%), LR model (AUC = 81.4%), and IV model (AUC = 78.2%). This research finds out that the applied methods are quite worthy for flood susceptibility modeling within the study area. In flood susceptibility modeling, method selection is not a serious challenge; the care should tend to the input parameter quality. Based on the AUC values, the FR model is comparatively better, followed by the AHP model for regional land use planning, flood hazard mitigation, and prevention purposes.

Published in Open Geosciences

ISSN: 2391-5447 (Online)
Publisher: De Gruyter
Country of publisher: Poland
LCC subjects: Science: Geology
Website: http://www.degruyter.com/view/j/geo

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