Developing an integrated risk management framework for agricultural water conveyance and distribution systems within fuzzy decision making approaches.

Irrigation canal networks, as the primary agricultural water conveyance and delivery systems, are exposed to a variety of hazards affecting the water distribution processes. This study, for the first time, develops a comprehensive risk management framework for the canal network through a Fuzzy Hierarchical method. In this regard, the risk is analyzed by a combination of probability, consequence, and vulnerability against identified hazards based on the hierarchical framework. The developed model is based on fuzzy numbers to consider the uncertainties arise from experts' opinion. To aggregate the calculated risk in the hierarchical framework, the Fuzzy Simple Additive Weighting (FSAW) approach was employed. To enhance the reliability of the water distribution system and decrease the risk of failure, six risk management alternatives are proposed based on the risk assessment results and the most significant hazards. To prioritize managerial scenarios, two sets of criteria were selected including quantitative criteria (consisting of cost of operation and risk reduction) and a qualitative set (compromising social and operational criteria). The risk management scenarios were prioritized based on two rational multi-criteria decision-making (MCDM) methods of a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW). Regarding different degrees of importance of the criteria, a pair-wise comparison was conducted by a group of experts to determine the relative weight of the criteria. According to the risk assessment results, the riskiest hazards are poor maintenance, seepage, unexpected event, drought, and vandalism of the structure. Moreover, employing the MCDM model in risk-based decision-making reveals that "maintenance improvement" is ranked as the first scenario, with score values of 0.851 and 0.237 employing the SAW, and TOPSIS approaches, respectively.