An Efficient Deep Learning Method for Typhoon Track Prediction Based on Spatiotemporal Similarity Feature Mining

Typhoon is one of the most destructive natural disasters, and it affects human society significantly. To reduce the negative impacts, many deep learning models for predicting future typhoon tracks have appeared. However, most of these models use all of the data they obtain as input, which may cause the diversity of typhoon tracks to have a negative impact on the prediction outcomes. In this paper, a joint method is proposed. The method mainly includes two parts: First, use a spatiotemporal similarity feature mining model to find out paths that are similar to the ongoing typhoon. Second, a deep learning model for processing sequence data is trained by these similar paths and then used for predicting the future track points’ latitude and longitude. The joint method bridges the gap in deep learning models’ ability to process spatial information and the shortcomings of spatiotemporal similarity feature mining models in predicting future data. In the experiment, we use a spatiotemporal similarity feature mining model to generate different input datasets by changing the number of similar paths in it, which can compare the model’s accuracy in different inputs. Also, real typhoon data recorded in the North West Pacific Ocean are used in the experiment. Through a comparison between the real path and prediction results in longitude and latitude, we find that 100–250 similar typhoon tracks as input have the best prediction effect in different tasks and are more accurate in long-term prediction.