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Development of a risk-based multi-criteria approach for watershed prioritization with consideration of soil erosion alleviation (case study of Iran)
Development of a risk-based multi-criteria approach for watershed prioritization with consideration of soil erosion alleviation (case study of Iran)
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Development of a risk-based multi-criteria approach for watershed prioritization with consideration of soil erosion alleviation (case study of Iran)
Development of a risk-based multi-criteria approach for watershed prioritization with consideration of soil erosion alleviation (case study of Iran)

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Development of a risk-based multi-criteria approach for watershed prioritization with consideration of soil erosion alleviation (case study of Iran)
Development of a risk-based multi-criteria approach for watershed prioritization with consideration of soil erosion alleviation (case study of Iran)
Journal Article

Development of a risk-based multi-criteria approach for watershed prioritization with consideration of soil erosion alleviation (case study of Iran)

2016
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Overview
The classification of erosion-prone areas is a pre-requisite for providing an efficient watershed management as well as soil conservation. Thus, a practical decision support tool is required to assign priority weights to different related criteria. In this article, 13 effective soil erosion criteria have been used to prioritize different sub-watersheds in Arangeh watershed, Iran. The criteria have been classified in four groups of soil, geomorphology, topography and climate categories. The ranking outcomes of sub-watersheds via Modified Pacific Southwest Inter-Agency Committee method were compared with induced ordered weighted averaging (IOWA) model which considered hierarchy-based attitude and risk-based attitude (RBA) criteria. Copeland aggregation method was also applied as a benchmark for the comparison. Kendall’s Tau, a nonparametric stochastic test, investigated that RBA-based IOWA with fairly optimistic coefficient ( α  = 0.5) is the most conclusive ranking model because of its highest correlation coefficient which was about 0.9. Finally, the hierarchical clustering method classified sub-catchments. According to the results, four priority groups were identified based on vulnerability of sub-watersheds to soil erosion.