Bakhtiarizadeh Ali, Najafzadeh Mohammad, Mohamadi Sedigheh
Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, 76315117, Iran.
Department of Ecology, Institute of Environmental Sciences, Graduate University of Advanced Technology, Kerman, 76315117, Iran.
Sci Rep. 2024 Dec 2;14(1):29933. doi: 10.1038/s41598-024-78812-6.
Determining situation of groundwater vulnerability plays a crucial role in studying the groundwater resource management. Generally, the preparation of reliable groundwater vulnerability maps provides targeted and practical scientific measures for the protection and management of groundwater resources. In this study, in order to evaluate the groundwater vulnerability of Kerman-Baghin plain aquifer, two developed indicators including composite DRASTIC index (CD) and nitrate vulnerability index (NVI) based on DRASTIC index were considered. Soft computing methods, including Gene Expression Programming (GEP), Evolutionary Polynomial Regression (EPR), Multivariate Adaptive Regression Spline (MARS), and M5 Model Tree (MTM5) have been used to provide formulations for prediction of NVI. Soft computing techniques were fed nine input parameters: depth to water level, net recharge, aquifer environment, soil environment, topography, effect of unsaturated area, hydraulic conductivity, land use, and potential risk related to land use. After calculating the vulnerability by soft computing methods, the results showed that the EPR model with Correlation Coefficient (R) of 0.9999 and Root Mean Square Error (RMSE) = 0.2105 has the best performance in the testing stage in comparison with MARS (R = 0.9966 and RMSE = 2.408), M5MT (R = 0.9956 and RMSE = 2.988), and GEP (R = 0.9920 and RMSE = 3.491). Although the EPR and GEP models have more complex mathematical computations than other soft computing models, the MARS and MT model that have quadratic polynomial and multivariable linear structures respectively, can be considered as the best alternative. According to the MARS model, the vulnerability of the region is divided into two categories: very low vulnerability (73.06%) and low vulnerability (26.94%). Overall, the statistical results of soft computing techniques were indicative of effective formulations for evaluating the DRASTIC index.
确定地下水脆弱性状况在研究地下水资源管理中起着至关重要的作用。一般来说，编制可靠的地下水脆弱性图可为地下水资源的保护和管理提供有针对性且切实可行的科学措施。在本研究中，为评估克尔曼 - 巴欣平原含水层的地下水脆弱性，考虑了两个开发的指标，即基于DRASTIC指标的综合DRASTIC指数（CD）和硝酸盐脆弱性指数（NVI）。已使用包括基因表达式编程（GEP）、进化多项式回归（EPR）、多元自适应回归样条（MARS）和M5模型树（MTM5）在内的软计算方法来提供预测NVI的公式。软计算技术输入了九个参数：水位深度、净补给量、含水层环境、土壤环境、地形、非饱和区影响、水力传导率、土地利用以及与土地利用相关的潜在风险。通过软计算方法计算脆弱性后，结果表明，与MARS（R = 0.9966，均方根误差RMSE = 2.408）、M5MT（R = 0.9956，RMSE = 2.988）和GEP（R = 0.9920，RMSE = 3.491）相比，相关系数（R）为0.9999且均方根误差（RMSE）= 0.2105的EPR模型在测试阶段表现最佳。尽管EPR和GEP模型的数学计算比其他软计算模型更复杂，但分别具有二次多项式和多变量线性结构的MARS和MT模型可被视为最佳替代方案。根据MARS模型，该地区的脆弱性分为两类：极低脆弱性（73.06%）和低脆弱性（26.94%）。总体而言，软计算技术的统计结果表明其在评估DRASTIC指数方面有有效的公式。
