## 4 Results and Discussion
### Techniques in Modeling GWL Changes
#### 4.2.1 ML Approaches and Performance Metrics  
Machine learning techniques have been used to forecast changes in groundwater levels and accounted for 45.3 percent (53) of the publications (Figure 8). The many papers that were included in the review yielded over 70 different algorithms (Table 5). Random forest (RF), artificial neural network (ANN), multilayer perceptron (MLP), support vector machine (SVM), long short-term memory (LSTM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), recurrent neural network (RNN), adaptive neuro-fuzzy systems (ANFIS), and metaheuristic algorithms such as differential evolution (DE) and the genetic algorithm (GA) were the dominating algorithms. Other ensembles of the aforementioned algorithms, such as extreme learning machine (ELM) crossbred with ant bee colony (ABC), improved grey wolf optimizer (IGWO), and whale optimization algorithm (WOA), were used. Singular spectrum analysis (SSA), support vector regression and support vector classification (SVR-SVM) were the other ensembles.  
The most commonly utilized algorithm was RF (20 studies), which was followed by ANN (15 studies). AdaBoost and XGBoost were employed in 9 and 8 studies, respectively, while SVM was utilized in 13 studies. In eight (8) and five (5) studies, respectively, ensembles and metaheuristic algorithms were applied. In 20 studies, various algorithms such as decision tree (DT), convolutional neural network (CNN), ZeroR, Fuzzy Logic (FL), and others were utilized. It should be noted that one article in the primary study can employ three or more ML approaches. Figure 9 summarizes the number of times ML approaches were employed in studies.  
In the primary research, statistical indicators were utilized to assess the accuracy of the ML models used to forecast groundwater level changes. According to our statistics, the root mean square error (RMSE) is the most widely utilized performance indicator for monitoring accuracies, accounting for 30.8 percent. Nineteen evaluation metrics were identified in the study. Table 6 shows the number of times performance metrics were employed and their proportions. The Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error (MAE), coefficient of correlation (R), coefficient of determination (R\({}^{2}\)), percentage bias (Pbias), mean square error (MSE), mean absolute percentage error (MAPE), and Pearson coefficient (PR) were also used to measure the accuracy of the models. Other metrics included Monte Carlo,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Predictor Variable** & **Type** \\ \hline Precipitation & Continuous \\ Temperature & Continuous \\ Streamflow & Continuous \\ Irrigation demand & Continuous \\ Rainfall & Continuous \\ Population growth & Categorical \\ Environmental regulations & Categorical \\ Land elevation & Categorical \\ Lithology & Continuous \\ Distance from river & Continuous \\ Plan and profile curvature & Categorical \\ Land use & Categorical \\ Sea level & Continuous \\ Evapotranspiration & Continuous \\ Soil moisture & Categorical \\ Humidity & Continuous \\ Canopy water & Continuous \\ Vegetation & Categorical \\ Sunshine & Continuous \\ Average wind & Continuous \\ GRACE data & Satellite data \\ ENVISAT ASAR (EA) & Satellite data \\ RADAR SAT-2 & Satellite data \\ \hline \hline \end{tabular}
\end{table}
Table 4: Common predictor variables from the primary study.  
normalized mean square error (NMSE), Shannon entropy, area under the curve (AUC), Akaike's information criteria (AIC), average squared error (ASE), area under the receiver operator characteristics curve (AUROC), Kling-Gupta efficiency (KGE), scatter index (SI), and mean absolute relative error (MARE).