Vulnerability Assessment of Tajan Watershed in Terms of Flood using BWM Method

Floods are one of the most important natural disasters that cause the most damage to infrastructure sectors and greatly affect people's economic and social activities. The occurrence of this phenomenon cannot be prevented, but by taking measures, its effects can be reduced to a great extent. One of the management methods to reduce damage caused by floods is to identify vulnerable areas. The purpose of this research is to determine the vulnerable areas of Tajen watershed using a new approach.

Therefore, in this study, with the aim of identifying flood vulnerable areas in four economic, socio-cultural, structural-physical and policy-institutional dimensions, the "Best of the Worst Method (BWM)" which is based on linear planning, for Index weighting was used. In order to rank the criteria, Friedman test was used. The statistical population of the study is the villages at risk of flooding in Tajan watershed in Mazandaran province. To do this, 208 questionnaires were randomly completed by residents of 40 villages. In this study, in order to analyze the questionnaires and implement the BWM method, SPSS16 software and Lingo18 optimization model were used, respectively.

The results of Friedman statistical test showed that there was a significant difference (Sig = 0.00) between the criteria studied in this study. The economic criterion with a rank of 2.85 has the highest importance compared to the other three criteria. Also, the results of weighting the indicators using the BWM method, which are based on the preference of the best and the worst, also showed that in the economic dimension of the agricultural income dependence index (0.284), in the structural-physical dimension of the index, there are appropriate communication channels (road and bright) (0.243), in the policy dimension, the index of destruction of natural resources (0.379) and in the socio-cultural dimension, the index of access to health centers (0.391) have the highest weight and have a great impact on vulnerability in terms of respondents. Residents are flooded. Also, in this study, the value of incompatibility index was less than 0.1, which indicates the good accuracy of the BWM model in weighting vulnerability indices.

In order to reduce costs and reduce the complexity caused by the large number of criteria and sub-criteria affecting the vulnerability of an area to floods, the use of new multi-criteria decision-making methods based on linear programming is emphasized.