# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 3 Deep Learning Model for the Prediction of Ocean Weather
### Configuration of the Deep Learning Model  
Figure 7 shows the configuration of the deep learning model proposed in this study. In this study, channel image data (set of 2D images of ocean weather) were used as input data. The noise of the input data was removed using the denoising AutoEncoder, and the input data from which noise was removed were used as input data for the ConvLSTM. The denoising AutoEncoder proposed in this study was trained as a single model in combination with the ConvLSTM. In the denoising AutoEncoder, as shown in Figure 7, two layers for encoding and two layers for decoding were used. In the two layers for encoding, the rectified linear unit (ReLU) was used as an activation function to prevent gradient vanishing, and in the two layers for decoding, the ReLU and the sigmoid were used as activation functions, respectively. At the end of the denoising AutoEncoder, the sigmoid was used to prevent the omission of data. At the end of the deep learning model, the sigmoid was also used as the activation function for the final output. In general, the ReLU, which is widely used as an activation function, treats all negative numbers as 0. If we use it as an activation function in the output layer, data loss may occur. However, the sigmoid function can output the value according to the scale of the input data; hence, the output data can be preserved as the scale of the input data. In addition, the adaptive moment estimation (ADAM) [18], which is well-known for its good performance, was used for weight optimization in all models. The input data comprised eight parameters of ocean weather, which were composed of several channels. When using data with multiple channels, there is an advantage that the causality between data composed of channels is considered. However, in the case of data that are not strongly correlated, learning models may produce undesirable effects. Therefore, in Section 3.3, the data that are useful to configure as a channel and use as a learning model are analyzed.  
Figure 7: Configuration of the deep learning model for ocean weather.  
As shown in Figure 7, the channel image is first refined into the ConvLSTM through a denoising AutoEncoder. The denoising process is performed through the encoding layer of the second stage and the decoding layer of the second stage. When encoding and decoding are performed in the layer, a batch normalization step is additionally inserted to perform efficient denoising and prevent information escape. Batch normalization is a technique for improving the speed, performance, and stability of deep learning [19]. If the encoded image is used as an input to the ConvLSTM, the input data are entered in a compressed form; hence, the output trained through the ConvLSTM is not the same as the format of the input data. Therefore, the decoded image was used to make the input and output data the same. The denoised data are composed of data with the same dimensions as the initial input data and used as input for the ConvLSTM again. In this study, various ConvLSTM models were tested for ocean weather prediction, and their efficiency was analyzed. Details are discussed in Section 4.