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Optimisation of Multipurpose Reservoir Operation by Coupling Soil and Water Assessment Tool (SWAT) and Genetic Algorithm for Optimal Operating Policy (Case Study: Ganga River Basin)

机译:结合水土评估工具(SWAT)和遗传算法优化多功能水库调度,以优化运营策略(案例研究:恒河流域)

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Reservoirs are recognized as one of the most efficient infrastructure components in integrated water resources management. At present, with the ongoing advancement of social economy and requirement of water, the water resources shortage problem has worsened, and the operation of reservoirs, in terms of consumption of flood water, has become significantly important. To achieve optimal reservoirs operating policies, a considerable amount of optimization and simulation models have been introduced in the course of recent years. Subsequently, the assessment and estimation that is associated with the operation of reservoir stays conventional. In the present study, the Soil and Water Assessment Tool (SWAT) models and a Genetic Algorithm model has been employed to two reservoirs in Ganga River basin, India in order to obtain the optimal reservoir operational policies. The objective function has been added to reduce the yearly sum of squared deviation from preferred storage capacity and required release for the irrigation purpose. The rule curves that were estimated via random search have been discovered to be consistent with that of demand requests. Thus, in the present case study, on the basis of the generated result, it has been concluded that GA-derived optimal reservoir operation rules are competitive and promising, and can be efficiently used for the derivation of operation of the reservoir.
机译:在综合水资源管理中,水库被认为是最有效的基础设施组成部分之一。当前,随着社会经济的发展和对水的需求,水资源短缺问题变得更加严重,从洪水的消耗量来看,水库的运行已变得十分重要。为了获得最佳的油藏运行策略,近年来已经引入了大量的优化和模拟模型。随后,与储层的操作相关的评估和估计保持常规。在本研究中,土壤和水评估工具(SWAT)模型和遗传算法模型已被用于印度恒河流域的两个水库,以获得最佳的水库运行策略。添加了目标函数,以减少与首选存储容量和灌溉所需的释放量的年度平方偏差。已经发现通过随机搜索估计的规则曲线与需求请求的曲线是一致的。因此,在本案例研究中,基于生成的结果,可以得出结论:遗传算法得出的最优油藏调度规则具有竞争性和前途,并且可以有效地用于油藏调度的推导。

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