Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models

Sci Total Environ. 2018 Feb 1:613-614:1385-1400. doi: 10.1016/j.scitotenv.2017.09.210. Epub 2017 Oct 12.

Abstract

Soil erosion, every year imposes extensive damages to human beings by means of reducing soil productivity and filling reservoirs from sedimentation in Ghaemshahr Basin in Mazandaran Province, (Iran); therefore, identifying prone areas to soil erosion for preventive measures is essential in this basin. In this research, erodibility prioritization of sub-watersheds of Ghaemshahr Basin has done using morphometric parameters analysis and different multi-criteria decision making (MCDM) models such as simple additive weighing (SAW), VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), technique for order preference by similarity to ideal solution (TOPSIS), and compound factor (CF). For this purpose, Advanced Space Thermal Emission Radiometer (ASTER), a Digital Elevation Model (DEM) with spatial resolution of 30m used for extraction and analysis of 23 morphometric parameters including basic, linear, shape, and landscape. For validation of the MCDM methods, the indices of percentage of changes and intensity of changes were used. The results of prioritization of sub-watersheds indicated that in TOPSIS and CF models, sub-watershed 30 with 0 and 13.33 scores are located in first rank, respectively, which is known as the most prone sub-watersheds to erosion. Also, results showed that sub-watersheds in terms of susceptibility to erosion, in CF model has an one category namely Low; meanwhile, in TOPSIS and VIKOR models show four classes including low, moderate, high, and very high. In contrast, for SAW model there are three classes of moderate, high, and very high susceptibility. In general, the results showed that morphometric parameters have high efficiency in identification of erosion-prone areas and also VIKOR method has higher predictive accuracy than TOPSIS, SAW, and CF models.

Keywords: Ghaemshahr Basin; Morphometric parameters; Multi-criteria decision making; Prioritization.