Flood susceptibility mapping by best-worst and logistic regression methods in Mersin, Turkey.

Abstract

Flood disasters resulting from excessive water in stream beds inflict extensive damage. Floods are caused by the expansion of cities, the erosion of riverbeds, inadequate infrastructure, and increasing precipitation due to climate change. Floods cause great damage to agricultural areas and settlements. Regions that may be affected by floods should be identified, and precautions should be taken in these areas to prevent these damages. Flood susceptibility maps are produced for this reason. The purpose of this study was to construct a flood susceptibility map so that susceptible locations in Mersin may be identified. Firstly, 429 flood events were identified for the flood inventory map. Twelve conditioning factors, namely elevation, slope, distance to river, distance to drainage, drainage density, soil permeability, precipitation, land cover/land use, stream power index (SPI), topographic wetness index (TWI), aspect, and curvature were used to create flood susceptibility maps, applying logistic regression and best-worst methods. The flood inventory data were used to prepare susceptibility maps and test their consistency. The receiver operating characteristic (ROC) curve was used for consistency analysis. In logistic regression, 86% of floods were located within 20% of the study area that was categorized as high and very high susceptibility. According to the value of the area under the ROC curve (AUC), logistic regression had a 0.901 value. Land use, soil permeability, and elevation were the most important factors in the logistic regression method. In the best-worst method, 85% of floods were located within the 14% of the study area categorized as high and very high susceptibility. According to the AUC value, the best-worst method had a 0.898 value. Elevation, distance to river, and precipitation factors had the highest coefficient value in the best-worst method. Based on the AUC values, the flood susceptibility maps had a high prediction capacity.