## 5 Performance Evaluation  
GWL modeling is mainly divided into two categories with regards to time, i.e., long-term, and short-term. Long-term forecasting is of great importance in various domains, for instance urban planning and water resource management, which require years of data to learn long-range dependencies. Short-term prediction is usually conducted to study variations in patterns and trends in the input variables related to the problem, for instance climatic conditions in the case of GWL. Since the target value of GWL modeling is a constant value, regression models are used in such studies. Different evaluation metrics have been used in the literature to measure the efficiency of proposed models. However, it is important to select appropriate performance metrics as it measures how well a model's predictions compare against the true values. Root mean square error (RMSE), mean absolute error (MAE), relative error (RE), and coefficient of determination (R\({}^{2}\)) are the most common choices of researchers in the literature. Moreover, the peak elevation criteria (PEC), and low elevation criteria (LEC) are special performance measures to evaluate the model against critical parameters such as rainfall, groundwater, etc. in the case of GWL. However, most of the time RMSE and R\({}^{2}\) have been used in GWL modeling studies. Table 4 shows different performance evaluation measures used by different experts for GWL prediction.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & \multicolumn{2}{c}{**Performance Evaluation**} & **Prediction** & **Target Prediction** & **Year** \\ \hline [PERSON] et al. [72] & MAPE, RMSE, NSE, KGE & Weekly & GWL & 2022 \\ \hline [PERSON] and [PERSON] [87] & \(R^{2}\), RMSE & Annually & GWL fluctuations & 2021 \\ \hline [PERSON] and [PERSON] [88] & RMSE, BIAS & & & \\  & \(R^{2}\), rBIAS & Monthly & GWL & 2021 \\ \hline [PERSON] et al. [89] & MAE,\(R^{2}\), NSE, RMSE & Daily & GWL & 2020 \\ \hline [PERSON] et al. [90] & \(R^{2}\), MAE, MSE & Daily & GWL & 2020 \\ \hline [PERSON] et al. [78] & RMSE, NSE, MAE, PBIAS & Monthly & GWL & 2020 \\ \hline [PERSON] et al. [91] & \(R^{2}\) & Daily & GWL/SWL & 2020 \\ \hline [PERSON] et al. [92] & RMSE, R, MAPE & Daily & GWL & 2020 \\ \hline [PERSON] and [PERSON] [93] & \(R^{2}\), RAE, MAE, RMSE, RAE & Daily & GWL & 2020 \\ \hline [PERSON] et al. [94] & NMSE, \(R^{2}\), RMSE, & Monthly & GWL & 2020 \\ \hline [PERSON] et al. [95] & \(R^{2}\), NRMSE & Monthly & GWL & 2020 \\ \hline \end{tabular}
\end{table}
Table 4: Different performance evaluation measures used by different experts for GWL prediction.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**References** & **Deep Learning** & **GP** & **MODFLOW** & **ANFIS** & **ANN** \\ \hline [PERSON] et al. [78] & & & ✓ & \\ [PERSON] et al. [79] & & & ✓ & ✓ \\ [PERSON] et al. [80] & & & ✓ & \\ [PERSON] et al. [81] & & & ✓ & \\ [PERSON] et al. [2] & & ✓ & & \\ [PERSON] et al. [32] & & & ✓ & \\ [PERSON] et al. [68] & & & ✓ & \\ [PERSON] et al. [82] & & & & ✓ \\ [PERSON] et al. [83] & & & & ✓ \\ [PERSON] et al. [84] & ✓ & & & ✓ \\ [PERSON] et al. [85] & ✓ & & & \\ [PERSON] et al. [86] & ✓ & & & \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._