## 3 Materials and Methods
### Artificial Intelligence and Machine Learning Models  
Artificial intelligence (AI) has become involved in recent years with forecasting groundwater quality modeling methods, e.g., artificial neural network (ANN), evolutionary algorithm (EA), etc. Given its capacity to handle enormous volumes of data, it offers an alternate method for handling complicated nonlinear hydrological modeling [145; 146]. The proper water quality parameter selection is from every aspect crucial in order to attain suitable AI model training. In every strategy, actual, in situ datasets are to be directly compared with those of AI method testing [147]. AI is advantageous over non-AI models by reducing the time spent for data sampling and AI proneness to rapid nonlinearity identification patterns as regards input and output data [12]. AI models combined with metaheuristic optimization techniques demonstrate higher reliability in terms of capturing the nonlinearity of water quality parameters [147]. General categorizations of AI methodology for soft water quality estimation include ANN modeling, fuzzy logic (FL)-based models, support vector machine (SVM) models, machine learning (ML) and hybrid models. A machine learning (ML) model utilizes potentially either deterministic or stochastic methods in different sectors to obtain the proper results via prediction methodology. ML models, such as adaptive neuro-fuzzy inference system (ANFIS), deep learning (DL) [148], evolutionary computing (EC), ensemble learning (EL), hybrid modeling (HM) or even support vector machine (SVM), support vector regression (SVR), were introduced and developed for the water quality parameter prediction and thus groundwater quality classification for irrigation purposes [147; 148; 149; 150; 151]. ML models rapidly improve groundwater level forecasting, which is regarded as critical for any water management planning [13]. ANN exploits relationships between input and output datasets [147], and is produced via neurons, a computational model that excels at content addressable memory, machine learning, pattern recognition and optimization [151; 152]. ANN uses several algorithms, such as back-propagation neural network (BPNN), Levenberg-Marquardt back-propagation (LMBP) [12; 13], feedforward neural networks (FFNNs), subcategorized into single layer perceptron (SLP) and multi-layer perceptron (MLP) [147]. They are preferrable for predicting spatial distribution parameters in aquifer remediation [12]. ANN modeling was applied in numerous cases over various regions, e.g., in India [153; 154], for groundwater quality prediction. Deep neural network (DNN) within ANN modeling enables accurate estimates of nitrates and heavy metals, i.e., cadmium and chromium [155]. Because of their quicker optimization and capability for generalization, EA and SVM demonstrate higher predictive performance comparable with ANN and ANFIS [156; 157], albeit limited studies were completed regarding groundwater quality forecasting so as to enhance confidence about the proper effectiveness for complex hydrogeological system simulation. A major drawback is considered to be the long training time. Nonetheless, in the near future, the ANN methodology shall be improved, and the ANFIS technique, which combines ANN and fuzzy inference system advantages, shall play a major role. Furthermore, ANNs, e.g., back-propagation neural network (BPNN), Levenberg-Marquardt back-propagation (LMBP), and MLP algorithms, are the most preferable in AI groundwater modeling due to their accuracy [12]. ML models, in many cases, analyze landfill leachate quantity/quality. Thus, e.g., a hybrid artificial intelligence model (AIM) based on a grey wolf metaheuristic optimization algorithm and a two-stage extreme learning machine (ELM-GWO) is adopted to predict landfill leachate quality parameters in Iran. Other single-stage AIMs can be mentioned (MARS, MLPANN, ELM, etc. ), as well as two-stage AIMs (MLPANN-GWO and ELMGWO, etc.) [14].