## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Locations** & **Metrics** & **ETo** & **SWCl\({}_{1}\)** & **SWCl\({}_{2}\)** & **SWCl\({}_{3}\)** & **In-General** \\ \hline \multirow{4}{*}{Loc. 1} & MSE & 0.07 & 0.00074 & 0.00027 & \(4.94\times 10^{-5}\) & 0.0144 \\  & RMSE & 0.29 & 0.021 & 0.01 & 0.004 & 0.13 \\  & MAE & 0.115 & 0.02 & 0.013 & 0.0072 & 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 & 0.093 & 0.0004 & \(7.48\times 10^{-5}\) & \(1.86\times 10^{-5}\) & 0.018 \\  & RMSE & 0.3 & 0.02 & 0.0086 & 0.0043 & 0.137 \\ \cline{1-1}  & MAE & 0.23 & 0.012 & 0.005 & 0.002 & 0.05 \\ \cline{1-1}  & \(R^{2}\) & 0.91 & 0.92 & 0.98 & 0.99 & 0.96 \\ \cline{1-1}  & MBE & \(-\)0.024 & 0.0034 & 0.0006 & 0.0004 & \(-\)0.004 \\ \hline \multirow{4}{*}{Loc. 3} & MSE & 0.27 & 0.0002 & \(3.8\times 10^{-5}\) & \(7.9\times 10^{-6}\) & 0.056 \\ \cline{1-1}  & RMSE & 0.51 & 0.016 & 0.006 & 0.002 & 0.23 \\ \cline{1-1}  & MAE & 0.36 & 0.01 & 0.003 & 0.0016 & 0.69 \\ \cline{1-1}  & \(R^{2}\) & 0.93 & 0.97 & 0.99 & 0.99 & 0.98 \\ \cline{1-1}  & MBE & \(-\)0.06 & \(-\)0.0007 & \(-\)0.0007 & \(-\)0.00027 & \(-\)0.013 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of the LSTM model on the test set for each location. The model was trained on all datasets. depth increases. Adding some other variables to the input of the model, such as irrigation amount, and adding the real data to the simulated dataset may improve the accuracy of the predicted value of SWC in the upper layer (see Table A1). As the results show, the models trained with two datasets showed similar performance to the model trained with all datasets. The results also show that the model trained with datasets from different locations can be generalized to other locations. Figure 9: True values vs. Predicted values. The x-axis indicates true values, and the y-axis indicates prediction values by models. CNN models are capable of extracting features from raw data. The convolutional layers were added to the proposed models to investigate the performance of the CNN- LSTM model for predicting ETo and SWC. Table 7 shows the CNN architectures that were used to stack the LSTM models. First, the input sequence was divided into two subsequences, each with four-time steps. The CNN can interpret each subsequence with four-time steps, and the LSTM used a time series of the interpretations from the subsequences as input. The time-distributed layer wrapped the CNN model and allowed each layer to be applied to each subsequence [43]. The CNN model contained two convolutional layers with kernel sizes of 3 and 2, respectively, followed by an average pooling layer with a pooling size of 2. The number of filters remained the same as the number of nodes in the proposed BLSTM model, i.e., 512 filters in each convolutional layer. In this paper, the performance of the models was improved by using the function tanh and the average pooling layer instead of the layer Relu or Max Pooling (see Table 8). The performance and computational efficiency of the CNN-BLSTM models are shown in Tables 8 and 9. The BLSTM model outperformed the CNN-BLSTM model in terms of model performance and computation time. As shown in Table 9, the number of trainable parameters tripled in the CNN-BLSTM model compared to the BLSTM. Therefore, the model began to overfit due to the complexity of model. Figure 10 shows the \(RMSE\) and \(R^{2}\)- score during the training of the CNN-LSTM models.