# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 1 Introduction
### Related Works  
Several studies have predicted ocean weather using machine learning. [PERSON] et al. [4] predicted wave height using three methods: an artificial neural network (ANN), genetic programming (GP), and model tree (MT). For the ANN, a deep feed-forward network (DFN), which is a comparatively  
Figure 1: The necessity of self-prediction of the weather forecast. simple configuration, was used. The three methods were used to predict wave height in the Arabian Sea from 3 to 72 h. As a result of predicting the significant wave height after three days using three models, the mean absolute error (MAE) was found to be from 0.19 to 0.40 m. The study demonstrated that GP and MT are more competitive methods than ANN, as the time-series characteristics of the weather data are not considered using the basic DFN. [PERSON] et al. [5] studied about three parameters of weather data: significant wave height, peak spectral period, and mean wave direction. They used three models to predict weather data in Lake Ontario: an ANN, an adaptive-network based fuzzy inference system (ANFIS), and a fuzzy inference system (FIS). As a result of performing predictions for three ocean weather, the mean squared error (MSE) for the significant wave height was 0.14 to 0.18 m, the peak spectral period was 0.74 to 1.16 s, and the mean wave direction was 2543\({}^{\circ}\) to 2893\({}^{\circ}\), respectively. They concluded that the ANFIS model was marginally more accurate than the other models. However, they did not consider the time-series characteristics of the weather data. That is, they did not consider the characteristics of ocean weather that change over time. Similarly, [PERSON] [2] studied a method for hindcasting wave height in the Atlantic Ocean using various configurations of an ANN model. He found that the wind speed had the strongest effect on wave height prediction. As a result of predicting the wave height, the MSE was 0.051 to 0.072 m. As mentioned previously, this result has a similar problem regarding the weather data. [PERSON] et al. [6] predicted sea surface temperature (SST) using a long short-term memory (LSTM) model for coastal seas near China from 1 day to 1 month. They considered the characteristics of the periodicity of the data using the LSTM model. The result was compared with two classical regression methods: support vector regression and multi-layer perceptron regression. As a result of predicting the SST after three days by the proposed method, the root mean square error (RSME) was 0.18\({}^{\circ}\). The study showed that the LSTM was adequate for predicting the periodic data. [PERSON] et al. [3] predicted the significant wave height, wave period, and wave direction using multi-layer perceptron (MLP) and support vector machine (SVM) [7] models for Monterey Bay in the USA. As a result of predicting three ocean weather events with their model, the RMSE for the significant wave height was 0.41 to 1.62 m, the wave period was 0.45 to 4.08 s, and the wave direction was 12\({}^{\circ}\) to 104\({}^{\circ}\), respectively. They compared the results of the two numerical models, and the machine learning showed similar accuracy to a physics-based numerical model. As mentioned previously, various deep learning methods have been used to predict ocean weather. However, most of them do not consider the periodicity, and only limited information and areas were predicted. To provide ocean weather for shipping companies and classification societies, a general prediction model capable of predicting eight parameters of ocean weather worldwide is required. Therefore, in this study, an image-based prediction model was proposed. Table 1 summarizes and compares related works and this study.