# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 4 Applications
### Case Studies and Discussion
When comparing the four cases, as the look forward increases, the prediction error also increases. The average errors of Cases 3-7 are 3.1%, 4.6%, 4.6%, 5.6%, and 5.1%, respectively. For accurate prediction, look forward should be small. However, the accuracy relatively remained over a range of look forward. In a typical prediction model for the ocean weather, the longer the forecast period, the higher the error, as shown in Figure 13. However, as the results presented in this study, it can be seen that the error does not increase after a certain period. Unlike the general prediction model based on physical phenomena, the deep learning-based prediction model presented in this study is a data-driven prediction model. Therefore, even if the prediction period is lengthened, the error does not increase indefinitely by considering the periodic and seasonal characteristics of the data at the same time. When the same time was predicted in Cases 6 and 8, we determined whether the normal prediction or the shifting prediction was more accurate. As shown in Figure 12, shifting prediction is a method of predicting the next time point using the predicted data as input again and can easily predict the far future, regardless of the size of the look forward. Therefore, in this study, the accuracy of the two methods was compared through Cases 6 and 8. In Case 5, the look forward was set to 3 days (12 steps), and in Case 7, the result was predicted after 3 days by shifting; the model trained to predict after 6 h (1 step) was used. Cases 3 and 8 are the same learning model, but the error is much more  
Figure 13: RMSE of significant wave height for ten organizations [23]. significant when shifting prediction is performed. Case 5 showed an average error of 5.6%, but Case 7 showed a relatively high error of 10.0% because of the accumulation of errors during shifting. In Case 2, the SST and MWD errors were relatively high compared with the other data types. Since the errors accumulate and become more significant during shifting, in Case 7, SST and MWD are 23.1% and 18.3%, respectively, and the error was significantly increased compared with the other data types. By comparing the results of Cases 6 and 8, it can be observed that when predicting a relatively far future, it is more accurate to train the model as a normal prediction model than as a learning model for shifting prediction. When the data at the same time were predicted in Cases 8 and 9, the data intervals used in the two models were different. Cases 8 and 9 used an interval of 1 day (originally, 4 steps). In this way, even if the same look forward was used, there was an advantage that prediction could be performed considering a longer period (longer look back range). Case 6 was a model that predicted 7 days later (look forward was 28 steps) using 22.5 days of data (look back range was 90). In Case 8, the look back range was also 90. However, because the interval of the data used for training was one, it was a model that predicts 7 days later using data from 90 days. Case 6 had an average error of 5.1%. In Case 8, the average error was 5.6%. When using the same look back range, it is more accurate to have a dense data interval. However, the difference in the error was not as large as it was in other comparisons. In Cases 8 and 9, two models were compared in terms of accuracy. One was a learning model that consists of six channels of data in ECMWF, and the other one was a learning model where SST and five other parameters of data were divided into two models, as described in Section 3.3. The error of Case 8 was 5.6%, and the error of Case 9 was similar to that of Case 6, with an error of 5.1%. By comparing Cases 8 and 9, it was confirmed that it is more accurate to separate the model considering the causality. Therefore, this study consists of three models for predicting ocean weather. One was a single model of SST, and one was for the five remaining ECMWF data (MWH, MWP, MWD, WU, and WV). The last one was a model for HYCOM data (CU and CV).