Department of Irrigation and Reclamation Engineering, University of Tehran, Tehran, Iran.
Department of Water Sciences and Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Environ Sci Pollut Res Int. 2020 Aug;27(22):28183-28197. doi: 10.1007/s11356-020-09188-z. Epub 2020 May 15.
Simulation of groundwater quality is important for managing water resources and mitigating water shortages, especially in arid and semiarid areas. Geostatistical models have been used for spatial prediction and interpolation of groundwater parameters. Recently, hybrid intelligent models have been employed for the simulation of dynamic systems. In this study, hybrid intelligent models, based on a neuro-fuzzy system integrated with fuzzy c-means data clustering (FCM) and grid partition (GP) models as well as artificial neural networks integrated with particle swarm optimization algorithm, were used to predict the spatial distribution of chlorine (Cl), electrical conductivity (EC), and sodium absorption ratio (SAR) parameters of groundwater. Results of the hybrid models were compared with geostatistical methods, including kriging, inverse distance weighting (IDW), and radial basis function (RBF). The latitude and longitude values of observation wells and qualitative parameters in three states of maximum, average, and minimum were introduced as input and output to the models, respectively. To evaluate the models, the root mean squared error (RMSE), the mean absolute error (MAE), and CC statistical criteria were used. Results showed that in the hybrid models, NF-GP with the lowest RMSE and MAE and highest CC was the most suitable model for the prediction of water quality parameters. The RMSE, MAE, and CC values were 107.175 (mg/L), 79.804 (mg/L), and 0.924 in the average state for Cl; were 518.544 (μmho/cm), 444.152 (μmho/cm), and 0.882 for electrical conductivity; and were 1.596, 1.350, and 0.582 for sodium absorption ratio, respectively. Among the geostatistical models, the kriging was found more accurate. Using the coordinates of wells will eventually allow the NF-GP to be used for more sampling and replace the visual techniques that require more time, cost, and facilities.
地下水质量模拟对于水资源管理和缓解水资源短缺非常重要，特别是在干旱和半干旱地区。地质统计学模型已被用于地下水参数的空间预测和插值。最近，混合智能模型已被用于模拟动态系统。在这项研究中，基于神经模糊系统与模糊 C 均值数据聚类 (FCM) 和网格分区 (GP) 模型以及人工神经网络与粒子群优化算法相结合的混合智能模型，用于预测地下水氯 (Cl)、电导率 (EC) 和钠吸收率 (SAR) 参数的空间分布。混合模型的结果与地质统计学方法（包括克里金法、反距离加权法 (IDW) 和径向基函数法 (RBF)）进行了比较。观测井的经纬度值和最大值、平均值和最小值三种状态的定性参数分别作为输入和输出引入到模型中。为了评估模型，使用均方根误差 (RMSE)、平均绝对误差 (MAE) 和 CC 统计标准。结果表明，在混合模型中，NF-GP 的 RMSE 和 MAE 最低，CC 最高，最适合预测水质参数。Cl 的平均状态下的 RMSE、MAE 和 CC 值分别为 107.175(mg/L)、79.804(mg/L) 和 0.924；电导率的 RMSE、MAE 和 CC 值分别为 518.544(μ mho/cm)、444.152(μ mho/cm) 和 0.882；钠吸收率的 RMSE、MAE 和 CC 值分别为 1.596、1.350 和 0.582。在地质统计学模型中，克里金法被发现更准确。使用井的坐标最终将允许 NF-GP 进行更多的采样，并取代需要更多时间、成本和设施的视觉技术。
