Prediction Interval of Interface Regions: Machine Learning Nowcasting Approach

Khaled Alielden*, Enrico Camporeale, Marianna B. Korsós, Youra Taroyan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

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 large-scale 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 Long-short-term 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 multi-dimensions time series of different time scales. In this work, a 1D ensemble system comprised of a Long-short-term 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 STEREO-A 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.
Original languageEnglish
Article numbere2022SW003326
Number of pages19
JournalSpace Weather
Volume21
Issue number3
DOIs
Publication statusPublished - 16 Mar 2023

Keywords

  • Machine Learning in Heliophysics
  • COMPUTATIONAL GEOPHYSICS
  • Neural networks, fuzzy logic, machine learning
  • EXPLORATION GEOPHYSICS
  • Gravity methods
  • GEODESY AND GRAVITY
  • Transient deformation
  • Tectonic deformation
  • Time variable gravity
  • Gravity anomalies and Earth structure
  • Satellite geodesy: results
  • Seismic cycle related deformations
  • HYDROLOGY
  • Estimation and forecasting
  • Extreme events
  • Time series analysis
  • INFORMATICS
  • Forecasting
  • Machine learning
  • Temporal analysis and representation
  • INTERPLANETARY PHYSICS
  • Solar wind plasma
  • IONOSPHERE
  • MAGNETOSPHERIC PHYSICS
  • MATHEMATICAL GEOPHYSICS
  • Prediction
  • Probabilistic forecasting
  • Persistence, memory, correlations, clustering
  • Stochastic processes
  • OCEANOGRAPHY: GENERAL
  • Ocean predictability and prediction
  • Time series experiments
  • NATURAL HAZARDS
  • Monitoring, forecasting, prediction
  • NONLINEAR GEOPHYSICS
  • Probability distributions, heavy and fat‐tailed
  • Scaling: spatial and temporal
  • POLICY SCIENCES
  • RADIO SCIENCE
  • Interferometry
  • Ionospheric physics
  • SEISMOLOGY
  • Continental crust
  • Earthquake dynamics
  • Earthquake source observations
  • Earthquake interaction, forecasting, and prediction
  • Seismicity and tectonics
  • Subduction zones
  • SPACE PLASMA PHYSICS
  • Stochastic phenomena
  • SPACE WEATHER
  • Policy
  • Research Article

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