## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Locations** & **Metrics** & **ETo** & \(\textbf{SWCl}_{\textbf{1}}\) & \(\textbf{SWCl}_{\textbf{2}}\) & \(\textbf{SWCl}_{\textbf{3}}\) & **In-General** \\ \hline \multirow{4}{*}{Loc. 1} & MSE & 0.08 & 0.0004 & \(9.4\times 10^{-5}\) & \(2.3\times 10^{-5}\) & 0.016 \\  & RMSE & 0.28 & 0.021 & 0.009 & 0.004 & 0.129 \\  & MAE & 0.21 & 0.013 & 0.0061 & 0.0029 & 0.12 \\  & \(R^{2}\) & 0.94 & 0.9 & 0.94 & 0.98 & 0.98 \\  & MBE & 0.032 & \(-\)0.0013 & 0.0016 & 0.003 & 0.0072 \\ \hline \multirow{4}{*}{Loc. 2} & MSE & 0.1 & 0.00047 & \(7.8\times 10^{-5}\) & \(1.89\times 10^{-5}\) & 0.02 \\  & RMSE & 0.33 & 0.021 & 0.0088 & 0.0043 & 0.15 \\  & MAE & 0.25 & 0.014 & 0.006 & 0.0029 & 0.055 \\  & \(R^{2}\) & 0.89 & 0.91 & 0.98 & 0.99 & 0.95 \\  & MBE & \(-\)0.024 & 0.0034 & 0.0006 & 0.0004 & \(-\)0.004 \\ \hline \multirow{4}{*}{Loc. 3} & MSE & 0.35 & 0.0004 & \(7.9\times 10^{-5}\) & \(1.2\times 10^{-5}\) & 0.075 \\  & RMSE & 0.59 & 0.02 & 0.008 & 0.003 & 0.28 \\ \cline{1-1}  & MAE & 0.46 & 0.014 & 0.005 & 0.002 & 0.9 \\ \cline{1-1}  & \(R^{2}\) & 0.9 & 0.95 & 0.98 & 0.99 & 0.97 \\ \cline{1-1}  & MBE & \(-\)0.26 & 0.0012 & 0.0002 & 0.0005 & \(-\)0.004 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of the LSTM model on the test set for each site. The model was trained on two datasets and tested on the third dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Layer (Type)** & **Output Shape** \\ \hline Input & (Batch Size, 2, 4, 11) \\ TimeDistributed(Conv1D) & (Batch Size, 2, 4, N. Filters) \\ (kernel-size = 3, padding = same) & \\ Dropout 1 & (Batch Size, 2, 4, N. Filters) \\ TimeDistributed(Conv1D) & (Batch Size, 2, 4, N. Filters) \\ (kernel-size = 2, padding = same) & \\ Dropout 1 & (Batch Size, 2, 4, N. Filters) \\ TimeDistributed(Averagepooling) & (Batch Size, 2, 2, N. Filters) \\ (pool-size = 2) & \\ TimeDistributed(Flatten) & (Batch Size, 2, \(2\times\) N. Filters) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CNN used to stake to the LSTM model. The performance of the LSTM model was compared with the CNN model and two traditional machine learning models: SVR and Random Forest. The CNN architecture used for the comparison was the same as the CNN architecture built on the LSTM architecture, with the expectation that the time-distributed layers were removed and a dense layer was added in place of the LSTM layers to make the prediction (see Table 10). RF is one of the most powerful machine learning methods. The Random Forest consists of several Decision Trees. Each individual tree is a very simple model that has branches, nodes where a condition is checked, and if it is satisfied, the flow goes through one branch, otherwise through the other, and always to the next node until the tree is finished [22]. Support Vector Regression (SVR) is a supervised learning algorithm used to predict discrete values. The basic idea behind SVR is to find the best fitting line. In SVR, the best fitting line is the hyperplane that has the maximum number of input points [21]. A kernel in the SVR model is a set of mathematical functions that take data as input and transform it into the desired shape. They are generally used to find a hyperplane in a higher-dimensional space. The most commonly used kernels include Linear, Nonlinear, Polynomial, Radial Base Function (RBF), and Sigmoid. Each of these kernels is used depending on the dataset. In this work, the RBF kernel has been used [45]. Figure 10: The RMSE and \(R^{2}\)-score of CNN-BLSTM model during training. The **left side** shows the \(R^{2}\)-score and the **right side** shows the RMSE.