An Integrated Fuzzy Shannon Entropy and Fuzzy ARAS Model Using Risk Indicators for Water Resources Management Under Uncertainty

The water issue is undoubtedly one of the most fundamental challenges and controversial issues of the current century. These days, the best options for managing water resources can be chosen by considering several indexes, such as political, social, and environmental criteria. The overall goal of this research is to propose an integrated model of fuzzy Shannon entropy and Fuzzy Additive Ratio Assessment (ARAS) that uses risk indexes to manage water resources in drought conditions. To achieve the goal of this research, first, risk factors are identified and selected based on the literature review. In previous studies, risk indicators were employed for water resource management, separately. However, this paper extracted an extensive list of risk indicators from prior studies and employed all these indicators for water resource management. Furthermore, four scenarios for water resource management in Chaharmahal and Bakhtiari province are introduced according to the geographical characteristics, climate, economic and agricultural conditions in this province. Then, a questionnaire is designed and distributed among experts in the field of water resource management. After collecting data, the proposed method is implemented on the data. The fuzzy Shannon entropy method is used to determine the weights of risk indicators, while the fuzzy ARAS method is applied for ranking water resource management scenarios. The results of applying fuzzy Shannon entropy reveal that the three indicators of volume reliability, vulnerability, and sustainability of the water supply system, with weight values of 0.124, 0.119, and 0.118, respectively, are the most effective risk indexes. The results of implementing fuzzy ARAS show that changing the cultivation pattern with a score of 0.936 is placed in the first priority, reducing the demand of the agricultural sector with a score of 0.922 is placed in the second priority, and the type of irrigation system with a score of 0.896 is placed in the third priority, and the reduction of industrial and drinking water consumption with a score of 0.882 is placed in the fourth priority. Finally, the results of implementing the proposed model of fuzzy Shannon entropy and fuzzy ARAS reveal an increase in volume reliability in the field of cropping pattern change in the studied province.