# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 4 Applications
### Effect of the Denoising AutoEncoder  
The model described in Table 4 of Section 4.3 was trained using a model combining a denoising AutoEncoder and the ConvLSTM, as mentioned in Section 3.1. However, before that, we attempted to confirm the effect of the denoising AutoEncoder. For this, SST, which has a strong periodicity, was used. The results obtained using only the ConvLSTM model and those obtained using the denoising AutoEncoder and ConvLSTM models proposed in this study were compared. Prior to comparing the two models, to determine whether the delay prediction result occurs even when training SST, the look forward value was changed under the same conditions by using a single channel of SST as input data, and the accuracy was compared. Figure 8 shows the results \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Cases**} & **Input Data Range** & **Output Data** & **Channel Configuration** \\ \cline{2-4}  & **(Look Back Steps)** & **(Look Forward Steps)** & **(Channel Configuration)** \\ \hline Case 1 & 60 days (240) & After 6 h (1) & 1 channel (SST) \\ Case 2 & 22.5 days (90) & After 6 h (1) & 6 channels (SST, MWH, MWD, MW, WU, WV) \\ Case 3 & 22.5 days (90) & After 1 day (4) & 6 channels (SST, MWH, MWP, MWD, WU, WV) \\ Case 4 & 22.5 days (90) & After 2 days (8) & 6 channels (SST, MWH, MWP, MWD, WU, WV) \\ Case 5 & 22.5 days (90) & After 3 days (12) & 6 channels (SST, MWH, MWP, MWD, WU, WV) \\ Case 6 & 22.5 days (90) & After 7 days (28) & 6 channels (SST, MWH, MWP, MWD, WU, WV) \\ Case 7 & 22.5 days (90) & After 3 days (1-shifting) & 6 channels (SST, MWH, MWP, MWD, WU, WV) \\ Case 8 & 90 days (90) & After 7 days (7) & 6 channels (SST, MWH, MWP, MWD, WU, WV) \\ Case 9 & 84 days (84) & After 7 days (7) & 1 channel (SST), 5 channels (MWH, MWP, MWD, WU, WV) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Learning model configurations. of SST predictions for the Yellow Sea in South Korea for approximately a year in 2018. It shows the difference in prediction results according to the look forward value. When the look forward value was set to 4, 1 day later, a little difference was found between the observed value and the predicted value. However, evidently, the predicted value is similar to the shape when the observed value graph is slightly shifted to the right. When the look forward value is increased to 20 (5 days) and 40 (10 days), the delay prediction result can be clearly confirmed. Notably, the look forward value increases, and the delay prediction problem of the prediction result becomes more pronounced. This occurs because, as explained in Section 2.3, the learning model does not accurately grasp the data pattern because of the noise. As the look forward value increases, the farther the value to be predicted is from the current time when the prediction is made so that the current value can be scaled as it is and inferred as a prediction result. To address this problem, the noise should be removed by adding a denoising AutoEncoder to the ConvLSTM model. To analyze the effect of using the denoising AutoEncoder, SST of South Korea's Yellow Sea was selected as a prediction target, as in the previous example. A total of 8 years of data were used, and a training set of 6 years and a test set of 2 years were used. The data were only used for this section and are different from the data mentioned in Section 4.1. Figure 9 shows the result of SST prediction. The upper graph is the prediction result using only the ConvLSTM, and the lower graph is the prediction result using the denoising AutoEncoder as the input of the ConvLSTM model. In the test case, models predicting a month (30 days) from the present were trained and compared whether the delayed problem is clearly visible or not. The result shows the last 2 years (from November  
Figure 8: Results of predicting SST using the ConvLSTM according to look forward steps. 2016 to October 2018) of the predicted range, which was not used for training. The two data used in Figures 8 and 9 are the same.