## 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

Talks
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

### Talks : Time schedule

Monday October 24, 13:45 - 15:00, Earth Hall
 16:00 Probabilistic ensemble learning for flare forecasting and value-weighted assessment Guastavino, S et al. Oral Sabrina Guastavino[1], Francesco Marchetti[2], Michele Piana[1], Federico Benvenuto[1], Cristina Campi[1] [1] MIDA group, Dipartimento di Matematica, Università di Genova, Italy, [2] Dipartimento di Matematica, Università di Padova, Italy There are three technical aspects that may have significant impacts on the forecasting effectiveness of machine learning for flare prediction. First, the optimization of the network parameters depends on an appropriate choice for the loss function in the training phase; second the validation step requires to implement a stopping rule that allows the selection of the best epoch; finally, the assessment of the prediction power of the algorithm should account for the dynamical nature of the flare forecasting problem. This methodological talk shows that probabilistic score-oriented loss (SOL) functions allow an optimization of the network that automatically accounts for the kind of skill score used for the prediction assessment; then, it describes how ensemble learning can be used in the validation phase to realize epoch selection; and, finally, the talk introduces value-weighted skill scores for flare forecasting that give greater importance to the values of the prediction than to its quality. We will finally point out that these aspects are crucial both in feature-based machine learning and image-based deep learning. 16:15 A prototype for a PCA-NN model for TEC with space weather parameters as predictors: selection of a NN algorithm and a set of predictors Morozova, A et al. Oral Anna Morozova, Ricardo Gafeira, Teresa Barata, Tatiana Barlyaeva Instituto de Astrofísica e Ciências do Espaço, University of Coimbra, OGAUC, Coimbra, Portugal A PCA-NN model for the total electron content (TEC) for the midlatitudinal region (Iberian Peninsula) is based on the decomposition of the observed TEC series using the principal component analysis (PCA) and the reconstruction of the TEC parameters (the daily mean TEC and amplitudes of the daily PCA modes) with neural networks (NN). Different NN algorithms (feedforwards with weight backpropagation, convolutional, recurrent) were tested and the best sets of predictors (space weather parameters) were defined for each of the TEC parameters and for each of the NN types. Based on these results a prototype for a PCA-NN model for TEC is proposed. 16:30 Temporal Convolutional Network for Local Forecast of Precipitated Electron Energy Flux Bouriat, S et al. Oral Simon Bouriat[1], Mathieu Barthélémy[2], Jocelyn Chanussot[3] [1]IPAG, Gipsa-Lab, SpaceAble, [2]IPAG, CSUG, [3]Gipsa-Lab The \textit{new space} industry brought fresh challenges to the space weather community as new satellites often appear more vulnerable to the natural environment. A recent incident involving Starlink satellites’ loss caused by a geomagnetic storm shows the need for a better understanding of space weather-related hazards, especially in LEO where new constellations are about to be brought. One danger of solar activity is \textit{spacecraft charging}: the accumulation of charged particles on and inside satellites, triggering electrostatic discharge. Only a few satellites measure them, and there still is a lack of models to characterize them. The exponential growth in the use of AI techniques has shown tremendous results and is very promising to answer this need. In this context, our study aims at presenting the first results of using a temporal convolutional network (TCN) to nowcast and forecast precipitated electron energy flux as measured by the satellites of the Defense Meteorological Satellite Program. Our inputs are solar wind data propagated at the bow shock nose (BSN) from OMNIWeb platform, and our outputs are time-series of electron energy flux for a given Magnetic Local Time and Magnetic Latitude. Using high-resolution OMNIWeb data to forecast close-to-Earth variables is not new and has been done a lot in the past (Camporeale, 2019). Most time-series forecasts (e.g., geomagnetic indices) are performed using artificial neural networks (e.g., Lethy et al. 2018). However, in the past few years, some convolutional architectures appeared to outperform recurrent neural networks in completing sequence modelling tasks (Bai et al. 2018). One of the issues behind forecasting near-Earth time-series using Lagrange 1 or BSN data lies in the propagation lead time between the two locations. Most forecasting methods solve this by prefixing which inputs from the past should be kept (e.g., McGranaghan et al., 2021): for instance, keeping only the values at $t_0-1$hr and $t_0-2$hr. Another idea is to use a variable propagation lead time based on the solar wind velocity (Wintoft et al., 2017). A TCN solves this problem by considering almost all past values and by finding, by itself, the relevant past values that will influence a given outcome. This paper is intended to be a proof of concept of the use of convolutional architectures to handle a variable lead time. It suggests that TCNs convincingly perform across a broad range of Space Weather time-series modelling. 16:45 Forecasting hazardous geomagnetically induced currents for Spanish critical infrastructures by using AI Conde villatoro, D et al. Oral Daniel Conde Villatoro[1], Florencia Luciana Castillo[2], Veronica Sanz González[1], Carmen García García[1], Bryan Zaldivar Montero[1], Jose Enrique García Navarro[1], Carlos Escobar Ibáñez[1] [1]Instituto de Física Corpuscular (IFIC); [2]Heidelberg University In the last decades, our society has become more interdependent and complex than ever before. Local impacts can cause global issues, as the pandemic clearly has shown, affecting the health of millions of human beings. It is also highly dependent on relevant technological structures, such as communications, transport, or power distribution networks, which can be very vulnerable to the effects of solar activity and their associated events, such as solar flares and coronal mass ejections, which may provoke disturbances, interruptions, and even long-term damage to these technical infrastructures, with drastic social, economic and even political impacts. However, these phenomena and their effects are not yet well understood, and their forecast is still in the early stages of development. In particular, geomagnetically induced currents (GICs) produced by solar storms can have catastrophic consequences on modern technology. This work, which uses a multidisciplinary approach, aims to deeply understand and develop an early warning system to evaluate the impact of violent solar storms on Spanish critical infrastructures. Specifically, we are developing an advanced machine-learning based predictive model of the impact of future solar storms on the ground. This model consists of two distinct stages. First, we are using as input real-time data from the solar wind space probe ACE (located at the L1 point in space) together with data from the geomagnetic observatories of Ebro, San Pablo de los Montes and San Fernando in Spain, and Coimbra in Portugal to develop a deep-learning model taking into account past conditions to predict the variation of the magnetic field on the Earth's surface at different locations in the Iberian Peninsula. Second, we will feed these local predictions of time-variation of the magnetic field into a physical model of the 3D Earth's geoelectrical structure to generate the geoelectrical fields that drive the GICs. Thus, the ultimate goal is to provide a real-time prediction of the GICs from extreme geomagnetic storms on the Spanish critical infrastructures.