Application of the coupled TOPSIS–Mahalanobis distance for multi-hazard-based management of the target districts of the Golestan Province, Iran

Abstract

This study is aimed at producing an improved ranking method by coupling the technique for the order of preference by similarity to ideal solution (TOPSIS) and Mahalanobis distance (MD) to prioritize the districts of Golestan Province, northeast of Iran, with respect to the prevailed natural hazards. The main idea of this work is underpinned by introducing a method that is: (1) in accordance with holistic thinking by engaging different threatening natural hazards in order to rank different threatened targets and (2) harmonized by the probabilistic context of the natural hazards. Therefore, maximum entropy (MaxEnt), a well-known data mining model, was used to model the spatial pattern of flood inundation and landslide occurrence over the study area. The area under the receiver operating characteristic (AUROC) was used to assess the goodness of fit and prediction power of the used models. As a result, the MaxEnt model showed an outstanding predictive performance with the AUROC values of 0.889 and 0.903 for landslide and flood inundation modelling, respectively. Afterwards, the revised universal soil loss equation (RUSLE) was employed to model soil erosion. This model successfully estimated the average soil loss of the study area with a value of about 33 ton/ha/yr which is in the range reported by provincial natural resources organization (10-35 ton/ha/yr). Results revealed that highly susceptible areas to the landslide, flood inundation, and water erosion potentially account for about 11, 9.6, and 6.6% of the Golestan Province surface area. Lastly, the TOPSIS–MD, TOPSIS, and Simple Additive Weight (SAW) methods were used to prioritize the districts of the Golestan Province with respect to all three susceptibility maps. As a result, TOPSIS–MD was chosen as the well-performing ranking method for further environmental managerial actions due mainly to considering the strong correlations among the criteria. According to TOPSIS–MD results, Minoodasht, Ramian, and Gorgan districts were recognized as the most threatened districts, while Gomishan, Aq Qala, and Gonbad-e Kavous districts are located at a safe zone with respect to the studied susceptibility indices. The proposed TOPSIS–MD framework merits more studies and is applicable to any multi-criteria decision-making issue in any branch of science.