A Stochastic Analytical Hierarchy Process for Supporting Nonpoint Source Pollution Control

摘要

Nonpoint source (NPS) pollution occurs when water moves over land and washes away natural and man-made pollutants, eventually depositing them into water bodies. It has been recognized as a threat to water quality, ecosystem diversity, and human health. Many best management practices (BMPs) have been developed to prevent, control, and remediate NPS pollution; however, selection of the most sound and cost-efficient option(s) has been a challenge in practice which can be further complicated by the existence of uncertain information. In this study, an innovative stochastic analytical hierarchy process approach is developed to aid group decision making with higher confidence and thereby reduce uncertainty. A real-world case study for agricultural BMPs assessment based on experts' opinions is conducted to test the feasibility and efficiency of the proposed approach. Experts' opinions are aggregated to approximate beta-PERT distributions for pairwise comparisons. Monte Carlo simulation is applied to repeatedly generate comparison samples, calculate the normalized eigenvectors, and produce the final scores for each alternative as probability distribution functions. The results indicate that conservation tillage has the highest performance score (0.24-0.39) in most replications and its overlap with the second-best BMP was negligible. Constructed wetlands technology, along with fertilizer and pesticide management, appeared mostly in the second and third place with scores ranging from 0.17 to 0.30 and 0.16 to 0.26, respectively. As compared with the traditional analytic hierarchy process (AHP), the proposed approach can address the inherent uncertainty related to insufficient information about objectives, vagueness of criteria and alternatives, and poor selection of subject-matter experts in group decision-making problems.