Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
Department of Geology, Payame Noor University, Tehran, Iran.
Environ Sci Pollut Res Int. 2022 Aug;29(37):56828-56844. doi: 10.1007/s11356-022-19762-2. Epub 2022 Mar 26.
Due to limited groundwater resources in arid and semi-arid areas, conjunctive use of surface water and groundwater is becoming increasingly important. In view of this, there are needs to improve the methods for conjunctive use of surface and groundwater. Using numerical models, optimization algorithms, and machine learning, we created a new comprehensive methodological structure for optimal allocation of surface and groundwater resources and optimal extraction of groundwater. The surface and groundwater system was simulated by MODFLOW to reflect groundwater transport and aquifer conditions. The important Marvdasht aquifer in the south of Iran was used as an experimental study area to test the methodology. In this context, we developed an optimal conjunctive exploitation model for dry and wet years using two new evolutionary algorithms, i.e., whale optimization algorithm (WOA) and firefly algorithm (FA). These were used in combination with the group method of data handling (GMDH) and least squares support vector machine (LS-SVM) to estimate sustainable groundwater withdrawal. The results show that the FA is more efficient in calculating optimal conjunctive water supply so that about 61% of water needs were met in the worst scenario for surface water resources, while it was 52% using the WOA. By applying the optimal conjunctive model during the simulation period, the groundwater level increased by about 0.4 and 0.55 m using the WOA and FA, respectively. The results of Taylor's diagram, box plot diagram, and rock diagram with error evaluation criteria, i.e., root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE), showed that the GMDH (RMSE = 6.04 MCM, MAE = 3.89 MCM, and NSE = 0.99) was slightly better than LS-SVM (RMSE = 6.36 MCM, MAE = 4.50 MCM, and NSE = 0.98) to estimate optimal groundwater use. The results show that machine learning models are cost- and time-effective solutions to estimate optimal exploitation of groundwater resources in complex combined surface and groundwater supply problems. The methodology can be used to better estimate sustainable exploitation of groundwater resources by water resources managers.
由于干旱和半干旱地区地下水资源有限，地表水与地下水的联合利用变得越来越重要。有鉴于此，需要改进地表水与地下水联合利用的方法。本研究使用数值模型、优化算法和机器学习，为地表水和地下水资源的优化配置和地下水的优化开采创建了一种新的综合方法学结构。通过 MODFLOW 模拟地表水与地下水系统，以反映地下水运移和含水层条件。伊朗南部重要的马尔瓦达斯ht 含水层被用作实验研究区，以检验该方法。在这方面，我们使用两种新的进化算法，即鲸鱼优化算法（WOA）和萤火虫算法（FA），开发了一个用于干、湿年的最优联合开采模型。使用 GMDH 和 LS-SVM 结合这两种算法来估计可持续的地下水开采量。结果表明，FA 在计算最优联合供水方面效率更高，在地表水最差的情况下，可满足约 61%的用水需求，而 WOA 则可满足 52%的用水需求。通过在模拟期间应用最优联合模型，WOA 和 FA 分别使地下水位上升约 0.4 和 0.55 m。泰勒图、箱线图和误差评估标准（即均方根误差（RMSE）、平均绝对误差（MAE）和纳什-苏特克里夫效率（NSE））的岩图结果表明，GMDH（RMSE=6.04 MCM，MAE=3.89 MCM，NSE=0.99）略优于 LS-SVM（RMSE=6.36 MCM，MAE=4.50 MCM，NSE=0.98），可用于估计最优地下水利用。结果表明，机器学习模型是解决复杂地表水与地下水联合补给问题下估算地下水最优开采的经济有效的方法。该方法可用于帮助水资源管理者更好地估算地下水资源的可持续开采量。
