## 4 Experiment
### Experimental Setting  
In order to compare with the traditional tropical cyclone path forecasting methods and deep learning methods, we selected three traditional methods (a statistical forecasting method, a dynamical and numerical track prediction technique, and a Numerical Weather Prediction (NWP) model [40]) and four deep neural network methods (an RNN, a GRU, an LSTM, and an AE-GRU) for comparison. The statistical method could generate 24 h, 48 h, and 72 h forecasts based on multivariate statistical regression [41]. The Sanders model, a dynamical and numerical forecasting technique, can forecast for the range of 12, 24, 48, and 72 h tropical cyclone tracks, and it is run operationally at NHC [42]. The tropical cyclone track forecasting skill of operational NWP models and their consensus were examined for the Western North Pacific from 1992 to 2002, where the model experiment results based on [PERSON] et al. [43] were selected as the numerical model results for comparison in this study. A traditional deep neural network model consists of an RNN, a GRU, an LSTM, and an AE-RNN model. A naive RNN is a kind of recursive neural network that takes sequence data as the input and performs recursion in the sequence evolution direction, and all nodes are connected by a chain. Therefore, it has certain advantages in learning the nonlinear features of sequences [16]. Both GRU and LSTM are variant models of RNN, except that an LSTM has an input gate, an output gate, and a forget gate, but a GRU has an update gate and a reset gate. An AE-RNN is a fusion model based on an AE and an RNN [44]. In this model, the extracted features from the AE are used to train the RNN model. The inputs of these models are the same [10; 24]. The structures of these models have three layers and 100 nodes. To ensure the credibility of the comparison experiment, all models used the same dataset. The input of each model contained the spatial features [Longitude, Latitude] and meteorological features [VMAX, MSLP, RAD, RAD1, RAD2, RAD3, RAD4, RADP, RRP, MRD, GUSTS, EYE, basin, Levelof tc development, WINDCODE, SUBREGION, Depth MAXSEAS, DIR, SPEED, SEAS1, SEAS2, SAES3, SEAS4, SEAS]. The output was the predicted tropical cyclone landing location at the next timestamp. For the experiment, each model was implemented five times, and the maximum epoch was set to 100. The models' training parameters are shown in Table 3, where the loss function, batch size, learning rate, and training method of the RNN, LSTM, GRU, and AE-RNN models were the same as the parameter settings used in our proposed model (AE-GRU). To obtain the best results, two, three, and four GRU, RNN, and LSTM layers were tested each, and the numbers of neurons and the learning rate were adjusted correspondingly. However, the preliminary results showed that when the number of GRU, RNN, and LSTM layers was set to three and the number of neurons was set to 100, the prediction result was optimal. The training time of each deep learning model was around 1.5 h. The training loss and validation loss of our proposed model are shown in Figure 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline
**Datasets’ temporal span/data size** & Year 1945–2017/61,089 records \\
**Training set** & 54,981 records \\
**Testing set** & 6108 records \\
**Temporal dimension** & 6 h \\
**Past data** & 72 h \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The detailed experiment dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**CY** & **YYYYMMDDHH** & **Lat N/S** & **Lon E/W** & **VMAX** & **MSLP** & **TY** & **RAD** & **RADP** & **—** \\ \hline
**30** & 2013110106 & 70N & 1410E & 15 & 1010 & DB & 0 & null &... \\
**30** & 2013110112 & 71N & 1396E & 15 & 1010 & DB & 0 & null &... \\
**30** & 2013110118 & 70N & 1393E & 15 & 1007 & DB & 0 & 1008 &... \\
**30** & 2013110206 & 71N & 1370E & 20 & 1007 & DB & 0 & 1011 &... \\..