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A regret-based behavioral model for shared water resources management: Application of the correlated equilibrium concept

机译:基于遗憾的共享水资源管理行为模型:相关均衡概念的应用

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The competition over water use in shared water resources systems may lead to conflict. Conflict can lead to strategic behaviors with the consequence of "Tragedy of Common" in water resources. In this paper, a novel approach is proposed for the quantity and quality management of shared water resources using the Correlated Equilibrium (CE) concept. For the first time in water resources management studies, a Reinforcement Learning (RL)-based method, namely Regret Matching (RM), is proposed to simulate agents' behaviors. In the proposed methodology, an agent, which is responsible for water allocations, tries to reduce illegal water withdrawal from resources, using some non-mandatory and mandatory suggestions. This agent's objectives are leading the system towards social optimality (SO) and reaching the environmental sustainability goal. A modified RM algorithm is also developed for behavioral simulation in urban areas. The proposed methodology's applicability and efficiency are evaluated considering some criteria such as the concentration of the nitrate pollutant in groundwater, the groundwater table fluctuations, the rate of illegal water extraction from the groundwater, and the stakeholders' general satisfaction. The results of applying the methodology to the western part of the Tehran metropolitan area show its ability to deal with the water and treated wastewater allocation problems in urban areas and increase in the learning and cooperation among agents. According to the results, a meaningful decrease in nitrate concentration in the aquifer and an increase in groundwater table levels are observed. The results also indicate that the model could teach the stakeholders to act more responsibly towards protecting the environment and conserving shared water resources.
机译:共用水资源系统中使用的竞争可能导致冲突。冲突可能导致战略行为,后果是水资源中的“共同悲剧”的结果。本文采用了使用相关均衡(CE)概念的共用水资源的数量和质量管理,提出了一种新方法。在水资源管理研究中,提出了一种基于加强学习(RL)的方法,即遗憾匹配(RM),以模拟代理人的行为。在拟议的方法中,使用一些非强制性和强制性的建议,拟议对水分配负责的药物,这是对水分配的责任,试图减少非法戒烟。该代理人的目标是导致社会最优性(SO)并达到环境可持续发展目标。还为城市地区的行为仿真开发了一种修改的RM算法。考虑到地下水中硝酸盐污染物的浓度等一些标准,评估了所提出的方法的适用性和效率,是地下水的硝酸盐污染物的浓度,与地下水的非法水提取的速度,以及利益相关者的一般性满意度。将方法应用于德黑兰大都市区的西部的结果表明了其对城市地区的水处理和处理过的废水分配问题的能力,以及代理人之间的学习与合作。根据结果​​,观察到含水层中硝酸盐浓度的有意义降低,并且被观察到地下水表水平的增加。结果还表明,该模型可以教导利益相关者更负责任地行动,以保护环境和保护共享水资源。

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