## 5 Conclusions  
In this paper, we addressed the challenge of predicting tropical cyclone tracks under a big data environment. Specifically, a novel fusion deep neural network based on an AE and a GRU was proposed for tropical cyclone track forecasting. The AE layer applied in our proposed model could be used to extract some meteorological factors and trajectory features from the preprocessed data. In addition, it could eliminate data redundancies and increase the model's generalization capability. A GRU layer was then implemented to extract the nonlinear and complex temporal features from the time-series dataset and then predict tropical cyclone tracks. In general, the AE-GRU model could process the time-sequential data effectively and make an accurate track prediction. Comparative experiments were conducted using the JWTC dataset for the years 1945-2017. The results indicated that our proposed model performed better than a traditional statistic model, a dynamical and numerical model, and a numerical weather prediction model for 24, 48, and 72 h forecasting. In addition, compared with some deep learning models, including an RNN, an AE-RNN, an LSTM, and a GRU, the proposed model also incurred a lower prediction error, the average APE of which was 174.62 km for a 72 h forecast. Furthermore, all meteorological variables showed a positive effect on tropical cyclone track prediction. However, the variable maximum sustained wind speed had the greatest contribution.  
In general, the proposed deep learning model was suitable to make tropical cyclone track prediction using historical tropical cyclone landing position data and meteorological variables. However, some limitations remain. First, tropical cyclones with different intensities may affect the landing locations to some extent. Second, the model could only predict tropical cyclone tracks, without the capability of predicting the amount of precipitation, wind speed, and tropical cyclone intensity. Third, the model could predict at most a 72 h tropical cyclone track rather than a longer time range. In the future, we will improve our model by considering tropical cyclone intensities in the deep learning model and adding more datasets to predict more meaningful variables for a longer time period.  
Conceptualization, J.L., P.D., Y.Z., and J.P.; methodology, J.L., P.D., Y.Z., and J.P.; writing, original draft preparation, P.D.; writing, review and editing, J.L.; supervision, Y.Z.; project administration, J.P. All authors read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant Numbers 61572326, 61802258, and 61702333, the Shanghai Sailing Program under Grant Number 19 YF1436900, the Natural Science Foundation of Shanghai under Grant Number 18 ZR1428300, and the Shanghai Committee of Science and Technology under Grant Numbers 17070502800 and 16 JC1403000.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Typhoon** & **Avg APE** & **Max APE** & **Min APE** \\ \hline Location & 180.91 km & 345.11 km & 4.75 km \\ Location+ VMAX & 175.02 km & 344.97 km & 2.45 km \\ Location + MSLP & 176.29 km & 345.83 km & 2.10 km \\ Location + MRD & 177.64 km & 348.56 km & 2.54 km \\ Location + RADP & 176.20 km & 347.83 km & 1.96 km \\ Location + all variables & **174.62 km** & **344.96 km** & **1.91 km** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Avg APE, max APE, and min APE of different inputs.