# Typhoon Track Prediction Based on Deep Learning
## 3 Experiment Results and Analysis
### Comparison Experiments Level  
To show more intuitively whether the performance of the C-LSTM model proposed in this experiment is improved compared with the existing prediction models, a typhoon path prediction model based on LSTM is also constructed for comparison in this experiment. To better form a control experiment, the same data set, and parameters are used for this model and the C-LSTM model. In this experiment, the LSTM model is constructed to compare with the C-LSTM model, and the model parameters are adjusted by adjusting the loss function and optimizer to select the optimal parameters for the model. Because in the above experiments, the loss is the smallest and the best when the number of iterations is 100, the number of iterations of each model is also set to 100 in this  
Figure 6: Comparison of the real and predicted paths of Typhoon “Lionrock” under different iterations, where the blue path indicates the actual path of the typhoon and the orange path indicates the path predicted by the model. (**a**) The path comparison when the number of iterations is 25, (**b**) the path comparison when the number of iterations is 50, (**c**) the path comparison when the number of iterations is 100. p.s. The left side of the ocean represents Vietnam, where the non-English text indicates different cities in Vietnam; the right side of the ocean represents Hainan, China, where the non-English text indicates different cities in China. comparison experiment to facilitate the comparison of the prediction results. The prediction results of the two models using the surface distance error as the evaluation index with different parameter choices when the number of iterations is set to 100 are shown in Table 5.  
Through the experimental results in Table 5, we can see that the average surface distance errors of the C-LSTM-based typhoon path prediction models are all smaller than those of the LSTM-based typhoon path prediction models. This is probably because the CNN layer in the C-LSTM-based typhoon path prediction model allows the model to learn the features of the input data better, thus improving the model prediction accuracy. Moreover, we can see that the model with cross-entropy loss function and Adam optimizer performs the best among several C-LSTM-based typhoon path prediction models. To compare the errors between the C-LSTM typhoon path prediction model and the LSTM typhoon path prediction model and the real path of the typhoon, the comparison experiment also chose the typhoon \"Lionrock\" which will make landfall in 2021 as an example. The comparison experiment also chooses to take the typhoon \"Lionrock\" that will make landfall in 2021 as an example and plots its predicted path and the actual path on the world map for visualization. The specific comparison between the two models is shown in Figure 7, where the blue path indicates the real path of the typhoon, while the orange path represents the corresponding typhoon path predicted using the two different models. From Figure 7, we can see that although there are some errors between the predicted and true paths of the typhoon obtained by these two prediction models, they are generally consistent in terms of the general direction. For this typhoon, by using the surface distance error as the evaluation index and calculating the average surface distance error between them, we can get the average surface distance error between the predicted path and the true path obtained by the LSTM typhoon path prediction model is 11,503 m. The average surface distance error between the predicted path and the true path obtained by the C-LSTM typhoon path prediction model proposed in this paper is only 832 m. The average surface distance error between the predicted path and the real path is only 832 m.