Optimization of groundwater artificial recharge systems using a genetic algorithm: a case study in Beijing, China

Hao, Qichen; Shao, Jingli; Cui, Yali; Zhang, Qiulan; Huang, Linxian

Optimization of groundwater artificial recharge systems using a genetic algorithm: a case study in Beijing, China | Optimisation des systèmes de recharge artificielle des eaux souterraines en utilisant un algorithme génétique: un cas d’étude à Pékin, Chine Optimización de los sistemas de recarga artificial de agua subterránea utilizando un algoritmo genético: un estudio de Caso en Beijing, China 基于遗传算法优化地下水人工补给系统:以中国北京为例 Otimização de sistemas de recarga subterrânea artificial utilizando um algoritmo genético: estudo de Caso em Pequim, China

2018

Hao, Qichen | Shao, Jingli | Cui, Yali | Zhang, Qiulan | Huang, Linxian

An optimization approach is used for the operation of groundwater artificial recharge systems in an alluvial fan in Beijing, China. The optimization model incorporates a transient groundwater flow model, which allows for simulation of the groundwater response to artificial recharge. The facilities’ operation with regard to recharge rates is formulated as a nonlinear programming problem to maximize the volume of surface water recharged into the aquifers under specific constraints. This optimization problem is solved by the parallel genetic algorithm (PGA) based on OpenMP, which could substantially reduce the computation time. To solve the PGA with constraints, the multiplicative penalty method is applied. In addition, the facilities’ locations are implicitly determined on the basis of the results of the recharge-rate optimizations. Two scenarios are optimized and the optimal results indicate that the amount of water recharged into the aquifers will increase without exceeding the upper limits of the groundwater levels. Optimal operation of this artificial recharge system can also contribute to the more effective recovery of the groundwater storage capacity.

Mostrar más [+]