# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 4 Applications
### Case Studies and Discussion  
All accuracy for case studies was measured using \"Area 4\" out of the 12 areas shown in Figure 11. The training time for computing varies depending on the look back range and the channel, but on average, it took approximately a week to train one model. Table 5 shows the accuracy of 11 cases. Accuracy was covered for all areas, including land. Since the training was performed with the image data, some data that are not on the land, such as SST or MWH, are assumed to be 0, and an image is generated and used as input. In general, as the look back range increases, so does the prediction accuracy. However, the longer the look back range, the more memory allocation is required exponentially to train the models. If it is assumed to train the model for one area, the input data becomes an image of ocean weather during the look back range. Following the look back range, the number of images required for training \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & **SST Error** & **MWH** & **MWP** & **MWP** & **WU Error** & **WV Error** & **Average Error** \\  & [°K] & **Error [m]** & **Error [s]** & **Error [deg]** & **[m/s]** & **[m/s]** & **[\%]** \\ \hline \multirow{2}{*}{Case 1} & 1.84 & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{9.3} \\  & (9.3\%) & & & & & & \\ \multirow{2}{*}{Case 2} & 2.84 & 0.09 & 0.25 & 11.21 & 1.21 & 1.19 & 3.1 \\  & (9.3\%) & (0.6\%) & (1.4\%) & (3.1\%) & (2.0\%) & (2.1\%) & 3.1 \\ \multirow{2}{*}{Case 3} & 3.31 & 0.13 & 0.32 & 19.43 & 1.80 & 1.84 & 4.1 \\  & (10.8\%) & (0.8\%) & (1.4\%) & (5.3\%) & (2.9\%) & (3.2\%) & 4.1 \\ \multirow{2}{*}{Case 4} & 2.69 & 0.18 & 0.40 & 28.34 & 2.28 & 2.24 & 4.6 \\  & (8.8\%) & (1.2\%) & (2.3\%) & (7.8\%) & (3.7\%) & (3.9\%) & 4.6 \\ \multirow{2}{*}{Case 5} & 3.68 & 0.22 & 0.48 & 33.76 & 2.46 & 2.38 & 5.6 \\  & (12.1\%) & (1.5\%) & (2.8\%) & (9.3\%) & (4.0\%) & (4.1\%) & \\ \multirow{2}{*}{Case 6} & 3.16 & 0.18 & 0.42 & 32.15 & 2.33 & 2.24 & 5.1 \\  & (10.4\%) & (1.2\%) & (2.4\%) & (8.9\%) & (3.8\%) & (3.9\%) & 5.1 \\ \multirow{2}{*}{Case 7} & 7.03 & 0.28 & 0.95 & 66.12 & 3.41 & 3.46 & 10.0 \\  & (23.1\%) & (1.8\%) & (5.5\%) & (18.3\%) & (5.5\%) & (5.6\%) & \\ \multirow{2}{*}{Case 8} & 4.11 & 0.17 & 0.44 & 32.66 & 2.05 & 1.99 & 5.6 \\  & (13.5\%) & (1.4\%) & (2.5\%) & (9.0\%) & (3.4\%) & (3.8\%) & 5.6 \\ \multirow{2}{*}{Case 9} & 3.47 & 0.44 & 0.75 & 28.27 & 3.06 & 3.17 & 5.1 \\  & (4.2\%) & (3.5\%) & (4.7\%) & (7.8\%) & (5.0\%) & (5.2\%) & 5.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of estimation accuracy for all cases. increased significantly. In addition, if we use images of multiple channels, the number of images required for training is multiplied by the number of channels. If a multi-channel image is used as the input for the model using the lock back range of Case 1, training is impossible due to a memory problem. That is, if training is performed with the computing specifications in Table 3, training will not progress because of insufficient memory. Due to the limitation of computing power, it was determined that one channel was the maximum look back range for 240 steps in this study. In the case of Case 1, because training was performed with one channel of SST, there was not an insufficient memory issue. However, when training is conducted through one channel, the causality between each type of data cannot be considered. Therefore, to proceed with training in multiple channels, the look back range was reduced from the maximum allowed by the memory. Cases 3-7 set the look back range to 22.5 days (90 steps) and performed training using six channels (SST, MWH, MWP, MWD, WU, and WV) of data. In Case 2, look forward was set to 6 h (1 step), and in Cases 4, 5, 6, and 7, training was performed after 1 day (4 steps), 2 days (8 steps), 3 days (12 steps), and 7 days (28 steps).