# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 1 Introduction  
### Research Background  
Ocean weather is one of the key considerations for robust ship path planning. The resistance of a ship can be divided into the still water resistance exerted by principal dimensions and the hull form of the ship, and an additional resistance exerted by ocean weather. The still water resistance is estimated during the design of the ship, and the additional resistance changes each time, depending on the ocean weather. Therefore, ocean weather data are used to calculate the additional resistance. For calculating the additional resistance, ISO15016:2015 [1], which is a guideline for the calculation of ship power, can be used. According to this guideline, wave height, wave direction, wave period, wind speed, wind direction, current speed, current velocity, and sea temperature are required to calculate roughly the required power of the ship in the sea. By adding this resistance to the still water resistance, the total resistance of the ship can be calculated. Using the total resistance, the requiredpower and fuel consumption of the ship can be estimated. To accomplish this, shipping companies and classification societies obtain ocean weather from weather centers. Currently, with the introduction of new technologies, path planning is being implemented in various ways for shipping companies. Predicting ocean weather is also essential in this process. Under normal circumstances, forecast data from weather centers can be acquired via satellite communication. However, in situations where data transmission and reception are not stable, the weather should be predicted without outside assistance. In addition, typical weather centers provide approximately 6 weeks of forecast data. Satellite communications are rarely lost for periods longer than 6 weeks. However, if there is no reception of the forecast data for the ocean weather, even for a short period of time, we have no choice but to rely on predicted information which is received before the loss of connection. In addition, typical forecasting information has a characteristic that the longer the forecast period, the greater the error. Therefore, in order to get even slightly more accurate data, it is necessary to have its own prediction model. On the other hand, control centers also need to pay regular fees to receive real-time forecasting information from weather centers. Therefore, we can reduce these costs by building our own prediction model. Figure 1 shows the necessity of self-predicting ocean weather.  
In general, \"empirical and numerical methods are used for weather forecasting\" [2]. Therefore, each weather center has a prediction model that uses its own analysis and weather data stored for a long time. However, because shipping companies and classification societies do not have such data and long-term experience with weather forecasting, there are limitations in developing prediction models in the same way as weather centers. Therefore, \"as a requirement for any forecast, an appropriately representative model should be developed, calibrated, and validated\" [3]. To accomplish this, various methods have been proposed, including data-driven approaches. One of the methods that has been recently highlighted is deep learning. Deep learning is an effective approach to grasp the causality of data in situations with insufficient experience and to derive meaningful prediction models. Therefore, in this study, deep learning was used to predict ocean weather.