The application of multi-attribute decision making methods in integrated watershed management

The present investigation has centered on examining the imp act of vegetation-based management strategies on the socio-economic, physical, and ecological aspects of the Aghsu watershed in Golestan province, with a particular emphasis on mitigating soil erosion and flooding issues. This study implemented four biological interventions (namely contour furrows with seedling planting, reforestation, channel terraces with tree planting, and agroforestry) and identified 16 management scenarios based on the managerial issues of the area. To establish a hierarchy of management scenarios, the techniques of TOPSIS, SAW, linear assignment, and VIKOR were employed, in conjunction with four distinct weighting methodologies (namely, Shannon Entropy, AHP, ANP, and adjusted weight). The Borda method has been employed to amalgamate the ranking of the suggested technique. Based on the results obtained from all proposed methods for prioritizing scenarios, it has been confirmed that "Scenario 1" holds the lowest priority. The findings indicate that "Scenario 3" (Agroforestry) and "Scenario 6" (Agroforestry and Contour furrows with planting of seedling) represent the optimal scenario groups. The findings indicate that the amalgamation of two scenarios and scenarios that incorporate Agroforestry exhibit superior outcomes. The implementation of agroforestry is a noteworthy endeavor that warrants heightened attention, with other management pursuits being regarded as secondary priorities. Moreover, it can be asserted that the precise weighting of evaluation criteria plays a crucial role in prioritizing management scenarios. The precise computation of weights is imperative. The utilization of a scenario-based methodology in this study facilitates the prediction of the potential outcomes of biologic management interventions. The utilization of this methodology is postulated to enable watershed planners and stakeholders to select the optimal scenario from the proposed set of management scenarios.