资源环境科技发展态势分析平台

This paper presents an image-based convolutional long short-term memory (convLSTM) machine learning algorithm to predict storm-time ionospheric irregularities. Unlike existing methods that are either focused on irregularities at individual locations or treat the irregularity prediction as a classification problem, the convLSTM-based architecture forecasts an entire regions’ ionospheric irregularity occurrence and intensity values. We implemented the convLSTM-based model with a custom-designed loss function (convLSTM-Lc) that includes a dynamic penalty on the difference between the truth and the predicted rate of total electron content index (ROTI) maps. The convLSTM-Lc is trained with real ROTI data collected during January 1-August 7, 2015 from ∼550 GNSS receivers located in (45^o-90^o N, 0^o-180^o W). Test results show that the convLSTM-Lc algorithm can forecast irregularity structures more accurately than a convLSTM model that implements conventional loss functions. The model also outperforms the convLSTM-L1, convLSTM-L2 and persistence models according to statistical classification metrics with a lead time of up to 60 minutes.