Investigating factors affecting the quality of water resources by multivariate analysis and soft computing approaches

This study used data from a large dam site to model changes in groundwater quality variables. Several indicators were investigated to check the quality of water sources for the site for different purposes. The factor analysis results displayed that four factors control 87.58% of water quality changes. The primary factor responsible for approximately half of the impact on water quality, accounting for 55.12% of the total variance, includes the EC, Ca²⁺, SAR, SO₄^-, Na⁺, CO₃^-, %Na, Cl^-, and TDS parameters. These parameters are directly related to water quality and are influenced by the natural characteristics of the region. Considering that the main control factor for water quality is the first factor mentioned, these factors were used in multivariate analysis and intelligent modeling. Therefore, Na⁺, Cl⁺, Na%, CO₃^-, and SO₄^2- were used as input variables (independent variables), and EC, TDS, and SAR were used as output variables (dependent variables). Support vector machine (SVM) with various kernel functions, multilayer perceptron artificial neural network (MLP-ANN) with various training algorithms, random forest algorithm (RFA), Gaussian process regression (GPR), and statistical analysis methods were used for modeling. Among the kernel functions used in SVM, the radial basis function (RBF) kernel provided the most accurate results. On the other hand, among the learning algorithms used in neural networks, the Levenberg-Marquardt algorithm demonstrated the highest level of accuracy. Modeling results based on error value, Wilmot agreement index, A₂₀ index, determination coefficient, and violin diagrams showed that the SVM (R² > 0.99, RMSE < 0.04, A₂₀ = 1.00, WAI = 1.00) achieved better than the other models. The results of Kruskal-Wallis's test disclosed that there is no substantial difference between the water quality parameters obtained from the models and the measured values.