Session CD1 - Artificial intelligence in the service of space weather

Elena Popova (Centro de Investigación de Astronomía, Universidad Bernardo O’Higgins, Chile), Robertus Erdelyi (University of Sheffield, Sheffield, UK), Marianna Korsos (Aberystwyth University, Aberystwyth, UK), Giovanni Lapenta (KU Leuven, Belgium)

Artificial intelligence is taking paramount importance in a wide range of applications from engineering to space physics. A particularly interesting area is big data and its associated applications. In the last few years, machine learning techniques have proven capable of forecasting space weather events with a much higher accuracy with respect to long-used traditional empirical and physics-based models.The direction of space weather uses big data, as an example, that are hard to handle with traditional methodology. It is hard to imagine the future of space weather without machine learning because more consideration is being given to the issues of reliability, uncertainty, and trustworthiness of machine learning models. O)n the practical sifde, the forecasts of the various physical processes are especially timely given the recent technology developments and the expansion of our technosphere. This session encourages submissions addressing the latest advances in the application of artificial intelligence and their application to space weather. Contributions are welcome from all areas of space weather that focus on the application of artificial intelligence, including forecasting various processes and analyzing satellite or ground-based data.

Poster Viewing
Monday October 24, 09:00 - 14:00, Poster Area

Monday October 24, 13:45 - 15:00, Earth Hall
Monday October 24, 16:00 - 17:00, Earth Hall
Tuesday October 25, 17:00 - 18:00, Earth Hall

Click here to toggle abstract display in the schedule

Talks : Time schedule

Monday October 24, 13:45 - 15:00, Earth Hall
13:45Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ DataRüdisser, H et al.Oral
14:00Advanced Image Preprocessing and Feature Tracking for Remote CME Characterization with Convolutional Neural NetworkStepanyuk, O et al.Oral
14:15Probing the coronal magnetic field with physics informed neural networksJarolim, R et al.Oral
14:30Using Neural Networks to improve the performance and forecasting skill of a solar wind modelBarros, F et al.Oral

Monday October 24, 16:00 - 17:00, Earth Hall
16:00Probabilistic ensemble learning for flare forecasting and value-weighted assessmentGuastavino, S et al.Oral
16:15A prototype for a PCA-NN model for TEC with space weather parameters as predictors: selection of a NN algorithm and a set of predictorsMorozova, A et al.Oral
16:30Temporal Convolutional Network for Local Forecast of Precipitated Electron Energy FluxBouriat, S et al.Oral
16:45Forecasting hazardous geomagnetically induced currents for Spanish critical infrastructures by using AIConde villatoro, D et al.Oral

Tuesday October 25, 17:00 - 18:00, Earth Hall
17:00Decontamination of proton flux measurements in the radiation belts with machine learningBernoux, G et al.Oral
17:15Convolutional Neural Networks for Automated ULF Wave Classification in Swarm Time SeriesAntonopoulou , A et al.Oral
17:30Ensemble Learning for Accurate and Reliable Uncertainty QuantificationCamporeale, E et al.Oral
17:45Short-term forecasting of Total Electron Content in South AmericaPerez bello, D et al.Oral
18:00A method to choose a mother wavelet for feature detection of VLF signals for Machine learning Shivali, V et al.Oral


1Surrogate Modeling for Faster Space Weather PredictionBaeke, H et al.Poster
5Can Machine Learning solve the „Bz Problem“ in Interplanetary Coronal Mass Ejections? Reiss, M et al.Poster
7A Comparative Study on New ML Approaches for F10.7 Time Series ForecastingMarcucci, A et al.Poster
8Using machine learning to predict the timing, magnitude, and impact of solar flares.Edward-inatimi, N et al.Poster
9Towards explanation of airglow variation by ML techniquesVarga, M et al.Poster
10Applications of artificial intelligence in studies of space weather.Asimopolos, L et al.Poster