Randa Natras, Dipl.-Ing.

Deutsches Geodätisches Forschungsinstitut und
Lehrstuhl für Geodätische Geodynamik (Prof. Seitz)

Postadresse
Arcisstr. 21
80333 München

Tel.: +49 (89) 23031-1277
Fax.: +49 (89) 23031-1240
randa.natras@tum.de

Research Area

Ionosphere modeling and forecasting, Machine Learning, Space Weather, GNSS/GPS

Academic Career

  • since 10/2019, Doctoral Candidate (DAAD) at Deutsches Geodätisches Forschungsinstitut der TU München (DGFI-TUM)
  • 08/2019 - 08/2020, Wissenschaftliche Hilfskraft Tutorin at the Technische Universität München
  • 04/2019 - 09/2019, Research stay (DAAD) at Deutsches Geodätisches Forschungsinstitut der TU München (DGFI-TUM)
  • 11/2017 - 07/2018, Research stay (OeAD) at Technische Universität Wien, Austria
  • 11/2016 - 10/2017, Engineer of Geodesy, Municipality of Travnik, Bosnia and Herzegovina
  • 2013 - 2016, Master Degree in Geodesy at the University of Sarajevo, Bosnia and Herzegovina (Dipl.-Ing.)
  • 08/2015 - 09/2015, Research Intern (IAESTE) at University of Oslo, Norway
  • 2010 - 2013, Bachelor Degree in Geodesy at the University of Sarajevo, Bosnia and Herzegovina (-Ing.)
  • ,

Functions/Memberships

International Association of Geodesy (IAG)

  • Inter-Commission Committee on Theory, Joint Study Group T.29 "Machine learning in geodesy", Member (since 2019)

Publications

2022

Barta V., Natras R., Srećković V., Koronczay D., Schmidt M., Šulic D.: Multi-instrumental investigation of the solar flares impact on the ionosphere on 05–06 December 2006. Frontiers in Environmental Science, 904335, 10.3389/fenvs.2022.904335, 2022 (Open Access)
Natras R., Halilovic Dz., Mulic M., Schmidt M.: Mid-latitude Ionosphere Variability (2013–2016), and Space Weather Impact on VTEC and Precise Point Positioning. Advanced Technologies, Systems, and Applications VII, Sarajevo, Bosnia and Herzegovina, 471–491 , 10.1007/978-3-031-17697-5_37, 2022
Natras R., Soja B., Schmidt M.: Machine Learning Ensemble Approach for Ionosphere and Space Weather Forecasting with Uncertainty Quantification. 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), 1-4, 10.23919/AT-AP-RASC54737.2022.9814334, 2022
Natras R., Soja B., Schmidt M.: Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting. Remote Sensing, 14(15), 3547, https://doi.org/10.3390/rs14153547, 2022 (Open Access)

2021

Natras R., Schmidt M.: Machine Learning Model Development for Space Weather Forecasting in the Ionosphere. CEUR Workshop Proceedings, 3052, 2021 (Open Access)

Posters/Presentations

2022

Barta V., Natras R., Sreckovic V., Koronczay D., Schmidt M., Sulic D.: Multi-instrumental investigation of the solar flares impact on the ionosphere on 05–06 December 2006. 18th European Space Weather Week (ESWW2022), Zagreb, Croatia, 2022 (Poster)
Barta V., Natras R., Sreckovic V., Koronczay D., Schmidt M., Sulic D.: Multi-instrumental investigation of the solar flares impact on the ionosphere on 05-06 December 2006. 8th IAGA/ICMA/SCOSTEP Workshop on Vertical Coupling in the Atmosphere-Ionosphere System, Sopron, Hungary, 2022
Barta V., Natras R., Sreckovic V., Koronczay D., Schmidt M., Sulic D.: Multi-instrumental investigation of the solar flares impact on the ionosphere occurring in December 2006. European Geosciences Union (EGU) General Assembly, Vienna, Austria, https://doi.org/10.5194/egusphere-egu22-5277, 2022, 2022
Le N., Männel B., Natras R., Sakic P., Deng Z., Schuh H.: Apply noise filters for better forecast performance in Machine Learning. European Geosciences Union (EGU) General Assembly, Vienna, Austria, https://doi.org/10.5194/egusphere-egu22-4039, 2022
Natras R., Halilovic Dz., Mulic M., Schmidt M.: Mid-latitude Ionosphere Variability and Modeling including Space Weather Impact on VTEC and PPP. Symposium in Geodesy and Geoinformatics, 13th Annual Days of BHAAAS in Bosnia and Herzegovina, Sarajevo, Bosnia and Herzegovina, online, 2022
Natras R., Soja B., Schmidt M.: Machine Learning Ensemble Approach for Ionosphere and Space Weather Forecasting with Uncertainty Quantification. 3rd URSI Atlantic / Asia-Pacific Radio Science Conference (URSI AT-AP-RASC 2022), Gran Canaria, Spain, 2022
Natras R., Soja B., Schmidt M.: Interpretable Machine Learning for Ionosphere Forecasting with Uncertainty Quantification. D4G: 1st Workshop on Data Science for GNSS Remote Sensing, Potsdam, Germany, 2022
Natras R., Soja B., Schmidt M.: Uncertainty Quantification for Ionosphere Forecasting with Machine Learning. International Workshop on GNSS Ionosphere (IWGI2022) - Observations,Modelling and Applications, Neustrelitz, Germany and online, 2022
Natras R., Soja B., Schmidt M., Dominique M., Türkmen A.: Machine Learning Approach for Forecasting Space Weather Effects in the Ionosphere with Uncertainty Quantification. European Geosciences Union (EGU) General Assembly, Vienna, Austria, https://doi.org/10.5194/egusphere-egu22-5408, 2022

2021

Natras R., Schmidt M.: Time-series Forecasting of Ionospheric Space Weather using Ensemble Machine Learning. Affinity Workshop Women in Machine Learning (WiML) at the Thirty-eighth International Conference on Machine Learning (ICML) 2021, online, 2021 (Poster)
Natras R., Schmidt M.: Ionospheric VTEC Forecasting using Machine Learning. European Geosciences Union (EGU) General Assembly, Online, 10.5194/egusphere-egu21-8907, 2021
Natras R., Schmidt M.: Ensemble Machine Learning for Geodetic Space Weather Forecasting. Scientific Assembly of the International Association of Geodesy (IAG) 2021, Beijing + online, 2021
Natras R., Schmidt M.: Machine Learning Model Development for Space Weather Forecast. Workshop on Complex Data Challenges in Earth Observation (CDCEO) 2021 at the 30th ACM International Conference on Information and Knowledge Management (CIKM), Online, 2021 (Poster)

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