In the current study, several soft-computing methods including artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), gene expression programming (GEP), and hybrid wavelet theory-GEP (WGEP) are used for modeling the groundwater's electrical conductivity (EC) variable. Hence, the groundwater samples from three sources (deep well, semi-deep well, and aqueducts), located in six basins of Iran (Urmia Lake (UL), Sefid-rud (SR), Karkheh (K), Kavir-Markazi (KM), Gavkhouni (G), and Hamun-e Jaz Murian (HJM)) with various climate conditions, were collected during 2004–2018. The results of the WGEP model with data de-noising showed the best performance in estimating the EC variable, considering all types of groundwater resources with various climatic conditions. The Root Mean Squared Error (RMSE) values of the WGEP model were varied from 162.068 to 348.911, 73.802 to 171.376, 29.465 to 351.489, 118.149 to 311.798, 217.667 to 430.730, and 76.253 to 162.992 μScm⁻¹ in the areas of UL, SR, K, KM, G, and HJM basins. The WGEP model's performance (R-values) for deep wells, semi-deep wells, and aqueducts of the areas of the KM basin associated with the arid steppe cold (Bsk) dominant climate classification was the best. Also, the WGEP's extracted mathematical equations could be used for EC estimating in other basins.

在当前的研究中，包括人工神经网络（ANN），自适应神经模糊推理系统（ANFIS），基因表达编程（GEP）和混合小波理论-GEP（WGEP）在内的几种软计算方法被用于对地下水的模拟。电导率（EC）变量。因此，位于伊朗六个盆地（Urmia Lake（UL），Sefid-rud（SR），Karkheh（K），Kavir-Markazi（ KM），Gavkhouni（G）和Hamun-e Jaz Murian（HJM））在2004–2018年期间收集了各种气候条件。考虑到各种气候条件下的所有类型的地下水，带有数据去噪的WGEP模型的结果显示出在估计EC变量方面的最佳性能。在UL，SR，K，KM，G和HJM盆地的区域为^-1。WGEP模型对与干旱草原冷（Bsk）优势气候分类相关的KM盆地区域的深井，半深井和渡槽的性能（R值）是最佳的。此外，WGEP提取的数学方程式可用于其他盆地的EC估算。