Water Policy (Jun 2022)
A framework for optimal rank identification of resource management systems using probabilistic approaches in analytic hierarchy process
Abstract
A resource management system is likely to succeed if stakeholders get involved in analyzing and choosing from the alternatives. The present work deals with multi-criteria decision models to evaluate rain water harvesting (RWH) structures. Standard practice is to acquire the weights for criteria from stakeholders using analytic hierarchy process (AHP) to predict the RWH structures' performance and rank them. Challenges in this process are that the data collection is laborious and time-consuming, considers limited stakeholders' opinions, and suffers from lower confidence factors. This work proposes a probabilistic approach to AHP using Monte Carlo simulation (MCS) to model uncertainty. The proposal is to collect multiple assessments instead of a single judgment from knowledgeable stakeholders (KSH) with customized questionnaires and to compute the relative importance of criteria using pairwise comparisons. Stochastically similar assessments within the range of these samples are then generated using different distribution functions to compute the performance of the RWH structures. The computed performance correlated well with common stakeholders' (CSH) opinions in the case study involving 10 existing RWH structures with seven different criteria, for all the distributions. The mean relative error with the proposed method is approximately 21% less than the existing point estimate method. HIGHLIGHTS Handles uncertainty using multi-criteria decision models and the Monte Carlo simulation method.; Input data modeling via range selection instead of a single value for the study.; Proposed a probabilistic AHP for analysis of RWH structures with a ranking approach.; Improved confidence in decision making toward RWH structures priority ranking.;
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