A new forecasting method of UHF-band ionospheric scintillations events by artificial intelligence based on a small dataset

Early warning of ionospheric scintillations is one of the main tasks of space weather forecasting. Faced with the forecasting information requirements of UHF-band ionospheric scintillation events over Chinese low-latitude region, the empirical knowledge and deep learning technique are used to identify the key precursors of ionospheric scintillations from the related ionospheric background parameters based on a small dataset. After that, the forecasting problem of post-sunset ionospheric scintillation events is converted to a classification problem easily solved by deep learning technique. Based on deep belief network of deep learning technique, a new forecasting method of UHF-band ionospheric scintillations events over Chinese low-latitude region has been established. After analyzing the correlations of different combinations of related observations and the occurrences of UHF-band scintillation events using this method, it is suggested that the latitudinal and daytime variations of the transequatorial total electron content (TEC) profile in the east of the forecasting area is one of the key precursors for forecasting UHF-band ionospheric scintillation events over Chinese low-latitude region, and is very helpful for the improvement of the forecasting performance.