# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 4 Applications
### Description of the Learning Models for Worldwide Data  
The disadvantage of not using the ConvLSTM described in Section 2.2 is that the characteristics of space and correlation between each parameter of ocean weather cannot be considered for learning. In addition, when using a conventional LSTM, an LSTM learning model must be created for each point where the prediction of ocean weather is desired. Figure 10 predicts MWH using the ConvLSTM. A total of 8 years of data were used for learning, 6 years were used as a training set, 1 year was used as a validation set, and the last 1 year was used as a test set. A model for predicting MWH after 7 days was trained using a total of 30 days of data. The batch size was 128, the number of hidden layers was 1, and the total number of nodes in the hidden layer was 32. The study was performed for 40 epochs until the model converged sufficiently. The lower graph in Figure 10 is an enlarged prediction result of the test set. The MAE of the test set was predicted relatively accurately at 0.78 m (4.8% error). Figure 9: Results of predicting SST using the ConvLSTM and denoising AutoEncoder. The conventional models of deep learning, such as DFN and LSTM, use data for specific locations to learn. Therefore, it is challenging to compare the ConvLSTM to DFN or LSTM directly. A large number of learning models are required to target worldwide data. Although the total number of models differs depending on how the resolution of the training model is set, 64,800 (=180 \(\times\) 360) models are needed worldwide to build a learning model for the ocean weather with resolutions of 1 degree in longitude and latitude. The recent increase in computing power has accelerated learning speed, but it takes considerable time to train 64,800 models. Therefore, in this study, the world was divided into 12 areas, and the proposed learning model was applied. Figure 11 shows the 12 areas used in this study. Dividing the latitude from \(-67.5^{\circ}\) to \(67.5^{\circ}\) into three equal parts and the longitude from \(-180^{\circ}\) to \(180^{\circ}\) into four equal parts divided the world into 12 equal pieces. The structure of the learning model is important. However, setting the input, output, and hyperparameters of the learning model has a significant influence also in the learning efficiency. Therefore, this study attempted to analyze its effects through case studies of various input and output  
Figure 11: The divided 12 areas for learning models. Figure 10: Results of predicting mean wave height (MWH) using conventional LSTM. changes. A total of 240, 90, and 84 input data (look back) were used. Since the time interval of the ocean weather is 6 h, if there are 240 input data, this indicates that 60 days of data are used as input. In the case of ocean weather, trends over time should be considered. Therefore, if possible, it is good to have a long look back period. However, lengthening the look back period infinitely is impossible because there are limitations on the computation power of memory. In this manner, we used six hours of data. Therefore, assuming 90 steps as a look back, and it means using 22.5 days of data as input. However, when the interval of data is set to 1 day, a total of 90 days of data can be used as a look back. The accuracy, according to the interval of the ocean weather, was also analyzed. In the case of output data (look forward), it denotes at which point the ocean weather is predicted through the look back data. In general, as the prediction time interval from the input data decreases, the accuracy increases. For example, predicting 1 day after the present is more accurate than predicting 1 week later. In this study, the accuracy was analyzed by changing the look forward to 4, 8, 12, and 28 steps. The look forward is similar to the look back. If the data interval is 6 h, 4 steps means a day later. Similarly, if the weather data are on a daily basis, 4 steps means 4 days later. In addition, in the case of look forward, shifting prediction is possible.