Typhoon multi-task prediction based on displacement residual learning
TL;DR
The proposed residual prediction method effectively solves the problem of insufficient utilization of environmental field features in short-term typhoon prediction and provides efficient and accurate deep learning technical support for typhoon disaster early warning.
Abstract
Aiming at the limitations that deep learning methods rarely integrate large-scale environmental field information in typhoon track and intensity prediction, and existing models have overfitting risks in absolute coordinate prediction, this paper proposes a residual prediction method for typhoon multi-task prediction. First, based on three architectures including Long Short-Term Memory (LSTM), Transformer and Multilayer Perceptron (MLP), a residual prediction strategy is introduced to convert absolute coordinate prediction into displacement increment prediction. Second, a multi-source dataset is constructed using the International Best Track Archive for Climate Stewardship (IBTrACS) and ERA5 reanalysis data, and 6-hour short-term prediction experiments are designed. With Mean Distance Error (MDE) and classification accuracy as the core evaluation indicators, performance comparison with baseline models is conducted. Experimental results show that the proposed method performs excellently in all models. Among them, the MLP Baseline achieves the optimal track prediction performance (MDE=46.47 km) and the optimal intensity classification performance (accuracy=91.62%); the LSTM+ERA5 fusion model reduces the track MDE by 4.65% compared with the LSTM Baseline, verifying the effectiveness of environmental field feature fusion. The proposed method effectively solves the problem of insufficient utilization of environmental field features in short-term typhoon prediction and provides efficient and accurate deep learning technical support for typhoon disaster early warning.