## 4 Conclusions  
The advantage of the recurrent neural network is its ability to process time-series data like agricultural datasets. In this paper, the ability of recurrent neural networks, including LSTM and its extension BLSTM, to model ETo and SWC was investigated. A drawback of deep learning methods is that they require a large amount of data to train. To overcome this problem, the simulated dataset was used for ET and SWC. The BLSTM model outperformed the LSTM model on the validation set, and hence a single layer BLSTM model with 512 nodes was trained. The proposed model achieved MSE in the range of 0.014 to 0.056, and the results show that the BLSTM model has good potential for modeling ETo and SWC. The CNN models are able to extract features from the data. A CNN model was added to the BLSTM model to extract features, and then BLSTM used these features to predict ET and SWC. When a CNN model was added to the LSTM model, and the number of parameters in the CNN-BLSTM was increased by three times, the model began to overfit after 100 epochs, and BLSTM showed better results than the CNN-BLSTM. Therefore, increasing the number of trainable parameters in deep learning algorithms may cause the model to overfit. The performance of BLSTM was also compared with Random Forest, SVR, and CNN. The best performance was obtained with BLSTM. The second-best performance was obtained with the CNN model. Among the machine learning methods, RF outperformed the SVR model. One disadvantage of BLSTM compared to CNN, SVR, and RF was that the training time of BLSTM was higher than the other models, but when trained, it was more accurate, and the computation time for prediction was almost the same as the other models. Finally, the importance of choosing a loss function was investigated. The model with MSE as the loss function performed better than the other models with an MSE of 0.056, and the model with \(H_{\delta=0.0001}\) as the loss function had the worst performance. These results show that the choice of a loss function depends on the problem and must be chosen carefully. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Loss Functions**} & \multicolumn{3}{c}{**Metrics**} \\ \cline{2-5}  & **MSE** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline MSE & 0.056 & 0.23 & 0.69 & 0.98 \\ RMSE & 0.067 & 0.25 & 0.8 & 0.975 \\ MAE & 0.07 & 0.07 & 0.27 & 0.97 \\ \(H_{\delta=1}\) & 0.063 & 0.25 & 0.79 & 0.976 \\ \(H_{\delta=0.1}\) & 0.068 & 0.26 & 0.77 & 0.974 \\ \(H_{\delta=0.01}\) & 0.074 & 0.27 & 0.8 & 0.972 \\ \(H_{\delta=0.001}\) & 0.062 & 0.27 & 0.07 & 0.977 \\ \(H_{\delta=0.0001}\) & 0.09 & 0.3 & 0.1 & 0.965 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance of the models using different loss functions for Loc. 3. In future work, the variables such as irrigation amount and groundwater level will be added to the input of the model to make the prediction more accurate. As mentioned earlier, one of the applications of the SWC and ET prediction model is to develop an end-to-end decision support system that automatically decides when and how much to irrigate. Deep reinforcement learning models are used to build such a system. An agent can be trained to select the amount of irrigation based on the condition of the field. Deep reinforcement learning algorithms require an environment that interacts with the agent and tells the agent the next state and reward. The SWC and ET prediction model is used as part of the algorithms' environment and determines the next state, which is the SWC and ET a day per head. K.A. : Investigation, Methodology, Writing--original draft, Writing--review. P.D.G. : supervision, writing--review, project administration and Funding acquisition. T.M.L. : Writing--Reviewing and Editing. All authors have read and agreed to the published version of the manuscript.