Prosiectau fesul blwyddyn
Crynodeb
Stream interaction region (SIR) is one of the space weather phenomena that accelerates the upstream particles of the interface region in interplanetary space and causes geomagnetic storms. SIRs are largescale structures that vary temporally and spatially, both in latitudinal and radial directions. Predicting the arrival times of interface regions (IRs) is crucial to protect our navigation and communication systems. In this work, a 1D ensemble system comprised of a Longshortterm memory (LSTM) model and a Convolution Neural Network (CNN) model—LCNN is introduced to classify the observed IR time series and give the prediction interval nowcast of its transit time to the observer. The outcomes of the two models are combined in a way to boost the accuracy of the predictor and prevent error propagation between them. The implemented technique is time series classification on datasets from STEREO A and B spacecrafts. The LCNN prediction system of IRs provides advanced Notice Time (NT) interval between [20, 160] minutes with sensitivity around 93% and geometric mean score gmean of 91.7%, and the skills decrease with increasing the prediction time. The LCNN demonstrates an enhancement in the prediction with respect to using only either the CNN or LSTM models. The predicted probabilities are recalibrated so that the predicted frequency of IRs becomes on average consistent with the observed frequency. Application of the method is useful to provide a classification of IRs by inputting a time series and estimating the likelihood of occurrence of an IR and its arrival time on the observer.
Key Points
Deep neural networks are used for time series classification and prediction
The adapted machine learning ensemble model is tailored for the Space Weather community, but applicable to many other communities
The adopted recalibration technique seems reliable for robust classification
Plain Language Summary
Stream interaction regions (SIRs) accelerate the upstream particles of the interaction/interface regions (IRs) in interplanetary space and cause geomagnetic storms. That raises the hazards on satellites' components and Earth's communication and navigation systems. Predicting the arrival time of IRs using machine learning regression techniques is challenging, particularly for such a complex problem of multidimensions time series of different time scales. In this work, a 1D ensemble system comprised of a Longshortterm memory (LSTM) model and a Convolution Neural Network (CNN) model—LCNN is introduced to classify the observed IR time series and give the prediction interval nowcast of its transit time to the observer. We used a nowcasting data set from STEREOA and B spacecrafts. The LCNN provides advance imminent IR at least 20 min and not longer than 160 min with a sensitivity of around 93% and the latter decreases with increasing the prediction time. The predicted probabilities are recalibrated so that the predicted frequency of IRs becomes on average consistent with the observed frequency. Application of the method is useful to provide a classification of IRs by inputting a time series and estimating the likelihood of occurrence of an IR and its arrival time on the observer.
Key Points
Deep neural networks are used for time series classification and prediction
The adapted machine learning ensemble model is tailored for the Space Weather community, but applicable to many other communities
The adopted recalibration technique seems reliable for robust classification
Plain Language Summary
Stream interaction regions (SIRs) accelerate the upstream particles of the interaction/interface regions (IRs) in interplanetary space and cause geomagnetic storms. That raises the hazards on satellites' components and Earth's communication and navigation systems. Predicting the arrival time of IRs using machine learning regression techniques is challenging, particularly for such a complex problem of multidimensions time series of different time scales. In this work, a 1D ensemble system comprised of a Longshortterm memory (LSTM) model and a Convolution Neural Network (CNN) model—LCNN is introduced to classify the observed IR time series and give the prediction interval nowcast of its transit time to the observer. We used a nowcasting data set from STEREOA and B spacecrafts. The LCNN provides advance imminent IR at least 20 min and not longer than 160 min with a sensitivity of around 93% and the latter decreases with increasing the prediction time. The predicted probabilities are recalibrated so that the predicted frequency of IRs becomes on average consistent with the observed frequency. Application of the method is useful to provide a classification of IRs by inputting a time series and estimating the likelihood of occurrence of an IR and its arrival time on the observer.
Iaith wreiddiol  Saesneg 

Rhif yr erthygl  e2022SW003326 
Nifer y tudalennau  19 
Cyfnodolyn  Space Weather 
Cyfrol  21 
Rhif cyhoeddi  3 
Dynodwyr Gwrthrych Digidol (DOIs)  
Statws  Cyhoeddwyd  16 Maw 2023 
Ôl bys
Gweld gwybodaeth am bynciau ymchwil 'Prediction Interval of Interface Regions: Machine Learning Nowcasting Approach'. Gyda’i gilydd, maen nhw’n ffurfio ôl bys unigryw.Prosiectau
 1 Wedi Gorffen

Solar System Physics at Aberystwyth University
Morgan, H., Cook, T., Gorman, M., Li, X., Pinter, B. & Taroyan, Y.
Science & Technology Facilities Council
01 Ebr 2019 → 31 Rhag 2022
Prosiect: Ymchwil a ariannwyd yn allanol
Y Wasg / Y Cyfryngau

Reports from Aberystwyth University Describe Recent Advances in Machine Learning (Prediction Interval of Interface Regions: Machine Learning Nowcasting Approach)
28 Ebr 2023
1 eitem o Sylw ar y cyfryngau
Y Wasg / Cyfryngau: Darllediadau'r cyfryngau