# Typhoon Track Prediction Based on Deep Learning
## 2 Materials and Methodology
### Typhoon Path Prediction Model Based on C-LSTM Network
#### 2.2.2 Model  
Considering the data consisting of the latitude and longitude where the typhoon center is located under different moments, i.e., the typhoon path, and the meteorological variables affecting its path are time-series in nature, and there are more meteorological variables. While CNN can extract the implicit features of multiple variables in the dataset and improve the generalization ability, LSTM can learn temporal features and improve the efficiency of the model in dealing with time series problems. Therefore, this experiment combines the two neural networks to propose a new C-LSTM network and use it for the prediction of typhoon paths.  
The C-LSTM model takes as input the typhoon path and the 7 meteorological variables selected by the Granger causality test. The data set is grouped by typhoon number, and the maximum length of each typhoon sequence is set to 20 data. When the sequence length of a typhoon exceeds 20, the excess is deleted. When the sequence length of a typhoon is less than 20, it is filled with zeros. Every 5 data in each group is a matrix, and the subsequent matrices are determined using the sliding window method. The matrix of this time series format is shown in Equation (9).  
\[M=\begin{pmatrix}lat_{i,t_{1}}&lon_{i,t_{1}}&F_{i,t_{1}}\\ lat_{i,t_{2}}&lon_{i,t_{2}}&F_{i,t_{2}}\\ \vdots&\ldots&\vdots\\ lat_{i,t_{5}}&lon_{i,t_{5}}&F_{i,t_{5}}\end{pmatrix} \tag{9}\]  
where \(F=\{f_{1},f_{2},\cdots f_{7}\}\) denotes the 7 meteorological variables selected by the Granger causality test.  
Then two convolutional layers were set up for extracting the feature vectors, the numbers of output channels were both 2. A Dropout layer was also added to prevent overfitting, and the LeakyReLU function was used for the start function. The output results of the CNN layer are then leveled, and the leveled results are input to the LSTM layer,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Meteorological Variables** & \(p\)**-Value (lon)** & \(p\)**-Value (lat)** & **Meteorological Variables** & \(p\)**-Value (lon)** & \(p\)**-Value (lat)** \\ \hline CAT & \textless{}1 & **\textless{}0.001** & R\_SHORT & \textless{}1 & \textless{}0.05 \\ TRACK\_TYPE & \textless{}0.05 & \textless{}1 & POCI & \textless{}1 & \textless{}0.05 \\ DIST2 LAND & **\textless{}0.001** & \textless{}0.05 & POCI & \textless{}1 & \textless{}1 \\ LANDFALL & **\textless{}0.001** & \textless{}0.1 & RMW & \textless{}1 & \textless{}1 \\ WIND & **\textless{}0.001** & **\textless{}0.001** & EYE & \textless{}1 & \textless{}1 \\ PRES & \textless{}0.1 & **\textless{}0.001** & GUUSTS & \textless{}0.05 & \textless{}1 \\ R\_DIR & \textless{}0.05 & \textless{}0.1 & SPEED & **\textless{}0.001** & \textless{}1 \\ R\_LONG & \textless{}1 & \textless{}0.1 & DIR & **\textless{}0.001** & **\textless{}0.001** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pairwise results of Granger causality tests between meteorological variables and typhoon paths (longitude, latitude).  
which is used to learn temporal features and output the typhoon location (longitude and latitude) for the next time stamp.