Typhoon Path Prediction using Deep Learning

Typhoons can be very destructive. Predicting their paths is important to be prepared when they arrive. In this blog post, I will talk briefly about an applied research topic which is to predict the paths of typhoons. This blog post is based on a recent research paper published in Neural Computing and Applications, where I have participated as co-author:

Xu, G., Xian, D., Fournier-Viger, P., Li, X., Ye, Y., Hu, X. (2022). AM-ConvGRU: A Spatio-Temporal Model for Typhoon Path Prediction. Neural Computing and Applications, Springer, 

Over the years many models have been proposed for typhoon path prediction. But the accuracy of these models could be improved. In general, we want to have models that are as accurate as possible.

Predicting typhoon paths is a difficult problem because it involves spatial data and temporal data, that is described using numerous features. Moreover, some features are 2D features while others are 3D features and combining them is also a challenge.

To address this issue, in the above paper, we presented a deep learning framework to perform accurate predictions of the paths of typhoons. The model is called Attention-based Multi ConvGRU (AM-ConvGRU).

For that research project, typhoons data was obtained from two sources: (1) the China Meteorological Administration (CMA) and (2) the European Centre for Medium-Range Weather Forecasts (ECMWF). The first provides data about 2D typhoons while the second provides 3D typhoon data. The data covers typhoons in the Western North Pacific (WNP) basin. Here is a visualization of typhoon paths from the paper:

After obtaining the data, the data has to be preprocessed. In particular, the 2D typhoon data is transformed into 53 features, according to a method called CLIPPER. These features are depicted in the table below as example.

Similarly, the 3D typhoon data has to been prepared. This is done by dividing the earth into a grid of 1 degree by 1 degree, by geopotential, and then looking more closely at the zone around the typhoon center. I will skip the details. But the result is a 3D time series structure:

After that, the deep learning model is trained using the 3d and 2D typhoon data. This is an overview of the model’s architecture:

I will skip the details.

To evaluate the proposed model, it was compared with state-of-the-art models. It was shown that the proposed model can generally provide better predictions.

To show a little bit more clearly what is the output, here is an illustration of some prediction by the proposed model, a baseline model, and to the historical path for Typhoon Mangkhut:

It can be seen that the proposed model is closer to the historical path than the baseline by over 50 km. Here is another example for Typhoon Talim:

The improvement of distance error for the proposed model over the baseline is over100 km.

Hope this has been interesting. This is just a very short overview of the topic of typhoon path prediction. If you are interested, please check the paper!

Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

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