Groundwater Quality Assessment for Drinking and Irrigation Purposes at Al-Jouf Area in KSA Using Artificial Neural Network, GIS, and Multivariate Statistical Techniques

Abstract

Groundwater is an essential resource for drinking and agricultural purposes in the Al-Jouf region, Saudi Arabia. The main objective of this study is to assess groundwater quality for drinking and irrigation purposes in the Al-Jouf region. Physicochemical characteristics of groundwater were determined, including total dissolved solids (TDS), pH, electric conductivity (EC), hardness, and various anions and cations. The groundwater quality index (WQI) was calculated to determine the suitability of groundwater for drinking purposes. The EC, sodium percentage (Na+ %), magnesium hazard (MH), sodium adsorption ratio (SAR), potential salinity (PS), and Kelley’s ratio (KR) were assessed to evaluate the suitability of groundwater for irrigation. Effective statistical tests and Feed-forward neural network (FFNN) modeling were applied to reveal the correlation between parameters and predict WQI. The results indicated that approximately all samples are appropriate for drinking and irrigation uses except samples of the Al Qaryat region. The ionic abundance ranking was Na+ > Ca2+ > Mg2+ > K+ for cations, and Cl− > SO42− > NO3− for anions. Moreover, the groundwater is dominated by alkali metals (K+ and Na+) and controlled by the rock–water interaction process. The indicators of groundwater quality for irrigation and drinking according to the following criteria (Na+ %, SAR, KR, MH, PS, WQI (WHO), and WQI (BIS)) can be predicted by the FFNN with root mean square errors (RMSE) of 0.136, 0.070, 0.022, 0.073, 2.45 × 10−3, 1.45 × 10−2, and 1.18 × 10−2, respectively, and R2 of 0.99, 1.00, 0.99, 0.99, 1.00, 1.00, and 1.00, respectively.

Modeling and Analysis of Cutoff Wall Performance Beneath Water Structures by Feed-Forward Neural Network (FFNN)

“…In a number of engineering applications, artificial neural networks (ANNs) simulate and forecast diverse environmental issues using statistical modeling [20][21][22][23]. According to Figure 3, the feed-forward neural network (FFNN) used in this study includes three layers and three levels.…”

“…In actuality, artificial neural networks (ANNs) are frequently utilized in a variety of civil and environmental engineering applications to test the consistency between the measured and projected concentrations of key parameters [20][21][22]. The acceptable performance of ANNs in modeling has two aspects: first, it enables theoretical analysis, and second, it offers a useful model to forecast the level of output parameters given comparable input data.…”

Activated Carbon Fabricated from Biomass for Adsorption/Bio-Adsorption of 2,4-D and MCPA: Kinetics, Isotherms, and Artificial Neural Network Modeling

“…In many different applications, the test criterion for neural network models often falls between 70 and 30 percent. This criterion has been extensively utilized in other research with related objectives [36]. A set of weights and biases for each randomly chosen connection were used to train the RBFNN-based model.…”

“…Artificial neural networks (ANNs) have gained popularity as the method of choice for modeling and predicting a wide range of environmental issues [36][37][38]. They offer several advantages over traditional methods, including the capacity to learn intricate input/output relations, parallel computing, and generalization.…”

“…They offer several advantages over traditional methods, including the capacity to learn intricate input/output relations, parallel computing, and generalization. ANN models have found success in environmental engineering applications, including for water resources and wastewater and water treatments [36]. To check the consistency between the projected and observed concentrations of important parameters, ANNs are widely used in a range of environmental applications [37,38].…”