# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 5 Conclusions and Future Works  
As explained in Section 1, there is a demand for shipping companies and classification societies to make a solution for predicting ocean weather on their own, which is currently only provided through weather centers. However, they were inexperienced in building a long-term, data-driven numerical model. Therefore, this study proposed a data-driven learning model for solving the lack of experience. In this study, we proposed a deep learning model that combines a denoising AutoEncoder and the ConvLSTM to predict ocean weather. With the ConvLSTM, we can train a model that predicts ocean weather over a wide range of regions by taking advantage of 2D data. That is, we can train the model over a wide area at once and consider the causality between points in the area. In addition, using data with noise removed by the denoising AutoEncoder as input, the problem of delay prediction, which frequently occurs when training time-series data, was solved. The method proposed in this study can be regarded as an initial study for the solutions to be provided for shipping companies or classification societies. In the case of the method of the ocean weather forecasting to be used in real solutions, the higher the accuracy, the better. However, currently, we are developing under the judgment that it is available if it has an error rate of around 5%. Through a variety of case studies, first, a model capable of training on the computer used in this study was selected. Second, analyzing the results of the learning model when changing the look back range, it was confirmed that as the look back range increased, the results were more accurate. It was expected that the prediction accuracy would decrease as the look forward increased. However, as a result of the prediction, it was confirmed that there was an exponential relationship, and the average error value converged to a value between 5 and 5.5. In addition, the results seemed to have an exponential relationship with the look forward value and the accuracy of the model, and it will be identified through further research. When comparing the predictions of the normal prediction and the shifting predictions, it was confirmed that the model that predicts a specific time point at once was more accurate than the shifting model when predicting the far future. It was confirmed that the accuracy was similar when the intervals of input data were varied to use the longer periodicity as input. Finally, it was determined that it is more accurate to build a model by dividing the model according to causality, rather than predicting all data as a single model. In this study, the world was divided into 12 areas to construct the worldwide learning model, and three learning models were trained for each area. The model proposed in this study was found to have satisfactory accuracy, with an average error of 6.7%. Although this study is in an early stage and the accuracy is not yet excellent enough to be commercially available, it has confirmed the possibility of applying the data-driven method to predict ocean weather worldwide. The goal of this study is to develop further and expand the proposed method to provide a more accurate and reliable forecast service for ocean weather. When using the learning model, it was found that there is still some room for improvement. In order to obtain the ocean weather at the beginning of the study, it was confirmed that NOAA, ECMWF, and HYCOM provided the ocean weather. Among them, ECMWF and HYCOM were accessed and used in this study. In the future, we will search for various providers and upgrade the input data to be used. As mentioned in Section 4.4, because of the lack of memory, the look back range was selected as 12 weeks. In the future, we intend to build more accurate models by increasing the look back range using a computer with better performance. In addition, currently, the land existed in 2D images was used as input data. In this case, for data types such as SST and MWH, if the value is not on the land, it was assigned a value of 0.