Optimizing the dam site selection problem considering sustainability indicators and uncertainty: An integrated decision-making approach

Abstract

Changes of climate, population growth, extended droughts, water scarcity, and sustainability considerations are amongst the concerns that stress more than before the significance of water resources management (WRM). One of the approaches that may assist decision-makers (DMs) in handling water resources is the proper site selection for dam construction. The dam site selection decision is a huge long-term investment and due to multiple decision-making factors, stakeholders or DMs, non-deterministic information in the decision-making process, and ripple effect on many other elements of the economy, meticulous analysis is needed. In this study, dam site selection problem (DSSP) is modeled in the form of binary programming under certainty, uncertainty, and hybrid circumstances. Hence, nadir compromise programming (NCP) model is executed in order to tackle the problem with crisp data. Further, a novel model based on NCP and stochastic programming is suggested to treat the uncertainty. The integration of the proposed models is able to address the problem with crisp and random data. The evaluation criteria are categorized based on sustainability (social, economic, and environmental) and technical indicators for dam site selection. To obtain the criteria weight, an interval group decision-making trial and evaluation laboratory (IGDEMATEL) approach is also applied where experts are able to state their opinions on linguistic terms and interval numbers. A real case study in Iran is then investigated in order to appraise the applicability of the developed methodology. A simulation method is further proposed to assess the obtained results and validate the suggested model's performance. Finally, the IGDEMATEL results are employed to survey the relationships between cause-and-effect criteria. The results obtained from the proposed and simulation models are similar in 83% of the cases, and the remaining difference of 17% is because of the uncertainty governing the problem, as well as the lack of sufficient data for precise decision-making.