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基于NSGA-Ⅲ算法与TOPSIS决策下的区域水资源多目标优化配置

Multi-objective optimization allocation of regional water resources based on NSGA-Ⅲ algorithm and TOPSIS decision

  • 摘要: 为缓解水资源供需紧张,指导区域用水管理,以社会、经济、生态综合发展为目标,供水量与需水量双向约束构建区域水资源优化配置模型.结合该模型特点,引入NSGA-Ⅲ算法对模型进行求解,以寻求Pareto前沿面与最优解集.同时,利用TOPSIS决策理论对众多Pareto可行解进行综合评价,选择权衡解作为最佳折中方案.将该模型应用于南阳市鸭河口灌区进行验证,对灌区来水频率P=75%时近期水平年与远期水平年进行水资源优化配置,并进行二次水资源供需平衡分析.结果表明,相对于基本方案,折中方案在近期水平年余水量增长至11 497.60万m3,可节约水资源3 272.38万m3;远期水平年由缺水4 279.28万m3变为余水3 950.30万m3,可节约水资源8 229.58万m3,节水效果明显,符合鸭河口灌区整体规划与用水需求.

     

    Abstract: In order to alleviate the tight supply and demand of water resources and guide the management of regional water resources, the optimal allocation model of regional water resources was constructed with the two-way constraints of water supply and water demand to help the comprehensive development of society, economy and ecology. According to the characteristics of the model, NSGA-Ⅲ algorithm was introduced to solve the model to find the Pareto front surface and the optimal solution set. In the face of many feasible Pareto solutions, TOPSIS decision theory was introduced to comprehensively evaluate different solutions, and the tradeoff solution was chosen as the best compromise solution. The model was applied to the Yahekou irrigation area of Nanyang City to verify the optimal allocation of water resources in the short-term and long-term horizontal years under the water inflow frequency of P=75%, and the balance of supply and demand of secondary water resources was analyzed. Compared with the basic scheme, the configuration results show that the annual residual water volume of the compromise scheme increases to 114.976 0 million m~3 at the recent level, and the water resource can be saved by 32.723 8 million m~3.The long-term level of water shortage has changed from 42.792 8 million m~3 to 39.503 0 million m~3, which can save 82.295 8 million m~3 of water. The water-saving effect is obvious, basically in line with the overall planning and water demand of the Yahekou irrigation area.

     

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