## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Locations** & **MSE** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) & **MBE** \\ \hline \multirow{3}{*}{CNN-LSTM (AveragePooling+tanh)} & Loc. 1 & 0.161 & 0.127 & 0.047 & 0.974 & \(-\)0.006 \\  & Loc. 2 & 0.019 & 0.138 & 0.051 & 0.96 & \(-\)0.003 \\  & Loc. 3 & 0.063 & 0.25 & 0.083 & 0.976 & \(-\)0.016 \\ \hline \multirow{3}{*}{CNN-LSTM (MaxPooling, Relu)} & Loc. 1 & 0.016 & 0.12 & 0.05 & 0.975 & \(-\)0.014 \\  & Loc. 2 & 0.021 & 0.14 & 0.055 & 0.95 & \(-\)0.017 \\ \cline{1-1}  & Loc. 3 & 0.61 & 0.24 & 0.082 & 0.977 & \(-\)0.012 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of the CNN-LSTM model on the test set. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Models** & **T(E/S)** & **N. Param.** & **N. E** & **N. P** & **Tr. S** & **val. S** & **Te. S** & **TT** \\ \hline CNN-LSTM & 2 & 6,842,885 & 160 & 20 & 6197 & 1550 & 365 & \textless{}1 s \\ LSTM & 1 & 2,151,429 & 250 & 20 & 6197 & 1550 & 365 & \textless{}1 s \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Computation efficiency of the LSTM and CNN-LSTM models. Table 11 shows the performance of each model on the test set. As shown, the LSTM models show better performance on the datasets. The CNN model has achieved the second-best performance. One of the applications of this model is a decision system that decides when and how much to irrigate in order to avoid water waste without compromising productivity. In future work, a reinforcement learning agent can be trained to select the best amount of irrigation. In deep reinforcement learning algorithms, there is an environment that interacts with an agent. During training, the agent chooses an action based on the current state of the environment, and the environment returns the reward and the next state based on the previous state to the agent. The agent tries to choose the action that maximizes the reward ([PERSON] et al., 2018). In the agricultural domain, the state of the environment can be defined as the climate data and the water content of the soil and ETo; the action is the amount of irrigation, and the reward is the net yield. An agent can be trained to choose the irrigation amount based on the state of the field, and the SWC and ET prediction model can be used as part of the environment of Deep Reinforcement Learning to calculate the next state of the environment. By using the trained model in this paper, the training of the agent can be end-to-end and does not require manual processing. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Param** \\ \hline Input & (Batch size, 8, 11) & 0 \\ (Conv1D)(kernel-size = 3) & (Batch size, 6, N. Filters) & 17,408 \\ Dropout 1 & (Batch size, 6, 512) & 0 \\ (Conv1D)(kernel-size = 2) & (Batch size, 5, N. Filters) & 524,800 \\ Dropout 1 & (Batch size, 5, N. Filters) & 0 \\ (Averagepooling)(pool-size = 2) & (Batch size, 2, N. Filters) & 0 \\ Flatten & (Batch size, 2 × N. Filters) & 0 \\ Dense & (Batch size, N. Filters) & 524,800 \\ Dense & (Batch size, 5) & 2565 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: CNN architecture used to compare to the LSTM model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Locations** & **Metrics** & **SVR-RBF** & **RF** & **CNN** \\ \hline \multirow{6}{*}{Loc. 1} & MSE & 0.2 & 0.085 & 0.033 \\  & RMSE & 0.45 & 0.29 & 0.18 \\  & MAE & 0.31 & 0.21 & 0.7 \\  & \(R^{2}\) & 0.81 & 0.92 & 0.94 \\  & MBE & 0.14 & 0.002 & 0.03 \\ \hline \multirow{6}{*}{Loc. 2} & MSE & 0.17 & 0.11 & 0.027 \\  & RMSE & 0.42 & 0.34 & 0.16 \\ \cline{1-1}  & MAE & 0.31 & 0.26 & 0.06 \\ \cline{1-1}  & \(R^{2}\) & 0.83 & 0.88 & 0.94 \\ \cline{1-1}  & MBE & 0.14 & 0.02 & \(-\)0.03 \\ \hline \multirow{6}{*}{Loc. 3} & MSE & 0.33 & 0.32 & 0.2 \\ \cline{1-1}  & RMSE & 0.58 & 0.56 & 0.45 \\ \cline{1-1}  & MAE & 0.39 & 0.39 & 0.16 \\ \cline{1-1}  & \(R^{2}\) & 0.92 & 0.92 & 0.93 \\ \cline{1-1}  & MBE & \(-\)0.023 & \(-\)0.02 & \(-\)0.016 \\ \hline \hline \end{tabular}
\end{table}