Javidi Sabbaghian Reza, Zarghami Mahdi, Nejadhashemi A Pouyan, Sharifi Mohammad Bagher, Herman Matthew R, Daneshvar Fariborz
Department of Civil Engineering, Ferdowsi University of Mashhad (FUM), Mashhad, Iran; Department of Biosystems and Agricultural Engineering, Michigan State University (MSU), East Lansing, MI, 48824, USA.
Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran; Department of Civil and Environmental Eng. and Tufts Institute of the Environment, Tufts University, Medford, MA, 02155, USA; Sociotechnical Systems Research Center, Massachusetts Institute of Technology, Cambridge, 02142 USA.
J Environ Manage. 2016 Mar 1;168:260-72. doi: 10.1016/j.jenvman.2015.11.038. Epub 2015 Dec 28.
Effective watershed management requires the evaluation of agricultural best management practice (BMP) scenarios which carefully consider the relevant environmental, economic, and social criteria involved. In the Multiple Criteria Decision-Making (MCDM) process, scenarios are first evaluated and then ranked to determine the most desirable outcome for the particular watershed. The main challenge of this process is the accurate identification of the best solution for the watershed in question, despite the various risk attitudes presented by the associated decision-makers (DMs). This paper introduces a novel approach for implementation of the MCDM process based on a comparative neutral risk/risk-based decision analysis, which results in the selection of the most desirable scenario for use in the entire watershed. At the sub-basin level, each scenario includes multiple BMPs with scores that have been calculated using the criteria derived from two cases of neutral risk and risk-based decision-making. The simple additive weighting (SAW) operator is applied for use in neutral risk decision-making, while the ordered weighted averaging (OWA) and induced OWA (IOWA) operators are effective for risk-based decision-making. At the watershed level, the BMP scores of the sub-basins are aggregated to calculate each scenarios' combined goodness measurements; the most desirable scenario for the entire watershed is then selected based on the combined goodness measurements. Our final results illustrate the type of operator and risk attitudes needed to satisfy the relevant criteria within the number of sub-basins, and how they ultimately affect the final ranking of the given scenarios. The methodology proposed here has been successfully applied to the Honeyoey Creek-Pine Creek watershed in Michigan, USA to evaluate various BMP scenarios and determine the best solution for both the stakeholders and the overall stream health.
有效的流域管理需要对农业最佳管理实践（BMP）方案进行评估，这些方案要仔细考虑所涉及的相关环境、经济和社会标准。在多标准决策（MCDM）过程中，首先对方案进行评估，然后进行排序，以确定特定流域最理想的结果。尽管相关决策者（DMs）呈现出不同的风险态度，但该过程的主要挑战是准确识别所讨论流域的最佳解决方案。本文介绍了一种基于比较中性风险/基于风险的决策分析来实施MCDM过程的新方法，该方法能选出最适合在整个流域使用的方案。在子流域层面，每个方案都包括多个BMP，其得分是根据从中性风险和基于风险的决策两种情况得出的标准计算得出的。简单加权平均（SAW）算子用于中性风险决策，而有序加权平均（OWA）和诱导OWA（IOWA）算子对基于风险的决策有效。在流域层面，汇总子流域的BMP得分以计算每个方案的综合优度测量值；然后根据综合优度测量值选择整个流域最理想的方案。我们的最终结果说明了在子流域数量范围内满足相关标准所需的算子类型和风险态度，以及它们最终如何影响给定方案的最终排名。这里提出的方法已成功应用于美国密歇根州的霍尼伊溪 - 派恩溪流域，以评估各种BMP方案，并为利益相关者和整体溪流健康确定最佳解决方案。
