Typhoon Tracks Prediction with ConvLSTM Fused Reanalysis Data

Abstract

Typhoon occurrences pose a great threat to people’s lives and property; therefore, it is important to predict typhoon tracks accurately for disaster prevention and reduction. In recent years, research using traditional machine learning methods has struggled to include temporal and spatial features. Moreover, research that has been conducted using satellite images only does not consider the influence of physical factors on typhoon movement; therefore, this paper proposes to add a convolutional layer to the Convolutional LSTM (ConvLSTM) model to improve the ability of the model to extract images. The previous positions of the typhoon’s center are marked on subsequent reanalysis images. The subsequent coordinates of the typhoon’s center are found by fitting the predicted coordinates of each physical variable. The research method in this paper required selecting the physical variables group which was most correlated with the direction and distance of the typhoon movement from 11 physical variables; this was achieved using Canonical Correlation Analysis (CCA) and Grey Relation Analysis (GRA). Then, reanalysis data is transformed into images and a continuous series of reanalysis image sequences is inputted into the ConvLSTM model so that it can make predictions. The mean absolute error of distance used for the ERA5 dataset, using the method proposed, was 54.69 km; thus, the validity of the model was proven.

Construction and Analysis of Space–Time Paths for Moving Polygon Objects Based on Time Geography: A Case Study of Crime Events in the City of London

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