Wetlands play an important role in the life of the planet. In order to preserve and revive the wetlands, decision-makers need to employ appropriate approaches and tools to identify the main driving forces and this could be an important step toward adopting effective strategies and making critical decisions. The current study used the combined analytic network process with driving forces-pressures-state-impacts-responses framework in mulino decision support system. The Parishan wetland, located in Iran, has been selected as the case study. After an initial study of the conditions of Parishan wetland and identifying the stakeholders, the opinions of experts and local residents were gathered to identify the most important parameters influencing the conditions of the wetland. The analytic network process model was used to improve and reduce the number of input parameters to mulino decision support system. Then, the most important and the best responses associated with the management of Parishan wetland were identified through questionnaires and imported into mulino decision support system along with other components. Finally, the mulino decision support system decision algorithm was run. The findings revealed that reduced precipitation, land-use change, competition of farmers in the wetland to use water resources, and increased temperature were the main driving forces. Moreover, water transfer from the Nargesi Dam was chosen as the most effective response. The results suggest that the driving forces-pressures-state-impacts-responses framework in mulino decision support system algorithm combined with analytic network process model can be used as an integrated tool to study and manage wetland socio-ecological ecosystems.

湿地在地球的生命中扮演着重要的角色。为了保护和恢复湿地，决策者需要采用适当的方法和工具来确定主要驱动力，这可能是采取有效策略和做出关键决策的重要一步。当前的研究在穆利诺决策支持系统中将分析网络过程与驱动力-压力-状态-影响-响应框架结合使用。案例研究选择了位于伊朗的Parishan湿地。在对Parishan湿地的状况进行初步研究并确定利益相关者之后，收集了专家和当地居民的意见，以确定影响湿地状况的最重要参数。解析网络过程模型被用来改善和减少向mulino决策支持系统输入参数的数量。然后，通过问卷调查确定与Parishan湿地管理相关的最重要，最佳的应对措施，并将其与其他组件一起导入mulino决策支持系统。最后，运行了mulino决策支持系统决策算法。研究结果表明，降水减少，土地利用变化，湿地农民竞争水资源使用以及温度升高是主要驱动力。此外，从纳尔吉斯（Nargesi）大坝调水被选为最有效的应对措施。