Research On Solar Flare Prediction Methods Using Sequential Data

Solar ?are is one of the most severe solar activities. It in?uences the space weatherand some activities on the Earth, so it is valuable to predict the level of solar ?ares.With the development of the observational instruments, large amounts of data is obtained.One of the most important scientific problems is how to extract knowledge and buildprediction model from the data. This problem is discussed and the main contributions ofthis dissertation are listed as follows:(1) Under the guidance of the modeling method, the dynamic characteristics ofthe prediction model are determined, and the prediction model with the sliding windowmethod is built. Comparing with the current prediction models, the proposed model,which can re?ect the evolutionary information of the photospheric magnetic field in theactive regions, is a dynamic model. Taking into account the evolutionary information ofactive regions, building a ?are prediction model can be viewed as a sequential supervisedlearning problem in machine learning. Here, the sequential supervised learning problemis transformed into the standard supervised learning problem, and the importance of thesequence of predictors is validated.(2) Multiscale predictors of photospheric magnetic field are proposed. In order tofully describe the in?uence of the evolution of photospheric magnetic field in active re-gions on the eruption of solar ?ares, multiscale predictors are constructed using maximumoverlap discrete wavelet transform and sequential feature extraction method. Compar-ing with the existing multiscale predictors extracted from photospheric magnetograms,the proposed multiscale predictors re?ect the evolutionary characteristics of photosphericmagnetic field. The predictability of the proposed multiscale predictors is quantitativelyestimated by information gain ratio, and the physical explanation of these predictors isgiven. Using these predictors, the solar ?are prediction model is built, and the effective-ness of these predictors is validated.(3) The uncertainty prediction model of solar ?ares is established. Because of thelimitation on the physical understanding of solar ?ares, the predictors probabilisticallyrelate to the eruption of ?ares. Bayesian network learned from the observational data isused to express these relationships, and the physical interpretation of this model is given. Comparing with existing models with the deterministic relationships, the uncertainty pre-diction model of solar ?ares is firstly developed. This model not only can be used toforecast the eruption of ?ares, but also can be used for knowledge discovery from theobservational data. For the increasing quantity of the observed data, this will become animportant research direction.(4) The concept of predictor teams is proposed, and the multiple prediction modelsbuilt by predictor teams are fused. The predictor team is firstly proposed and the reason-ability of the predictor team is explained. The base prediction models of solar ?ares arebuilt using predictor teams, and then these base models are fused to generate a compre-hensive prediction model.