Strong solar flare detection and its impact on ionospheric layers and on coordinates accuracy in the Western Balkans in October 2014

Natraš, Randa; Horozovic, Dzana; Mulić, Medžida

doi:10.1007/s42452-018-0040-9

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…, J m ) (Figure 2). Afterwards, the function is updated as in Equation (10). XGBoost [42] is an optimized gradient boosting algorithm that applies shrinkage technique as the regularization strategy to avoid overfitting.…”

Section: Ensemble Learningmentioning

confidence: 99%

“…VTEC exhibits latitudinal and longitudinal variations, diurnal, seasonal, semi-annual, and sunspot cycle variations, as well as coupling effects [5][6][7]. Furthermore, space weather can produce intense, irregular ionosphere variabilities, which can be difficult to model with traditional mathematical approaches and to properly minimize in positioning solutions, degrading positioning and navigation performances [8][9][10][11]. A complex chain of physical and dynamical space weather processes between the Sun, the interplanetary space, the Earth's magnetic field, and the ionosphere must be taken into account when modeling and forecasting these disturbances in the ionosphere.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting

2022

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Space weather describes varying conditions between the Sun and Earth that can degrade Global Navigation Satellite Systems (GNSS) operations. Thus, these effects should be precisely and timely corrected for accurate and reliable GNSS applications. That can be modeled with the Vertical Total Electron Content (VTEC) in the Earth’s ionosphere. This study investigates different learning algorithms to approximate nonlinear space weather processes and forecast VTEC for 1 h and 24 h in the future for low-, mid- and high-latitude ionospheric grid points along the same longitude. VTEC models are developed using learning algorithms of Decision Tree and ensemble learning of Random Forest, Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost). Furthermore, ensemble models are combined into a single meta-model Voting Regressor. Models were trained, optimized, and validated with the time series cross-validation technique. Moreover, the relative importance of input variables to the VTEC forecast is estimated. The results show that the developed models perform well in both quiet and storm conditions, where multi-tree ensemble learning outperforms the single Decision Tree. In particular, the meta-estimator Voting Regressor provides mostly the lowest RMSE and the highest correlation coefficients as it averages predictions from different well-performing models. Furthermore, expanding the input dataset with time derivatives, moving averages, and daily differences, as well as modifying data, such as differencing, enhances the learning of space weather features, especially over a longer forecast horizon.

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Section: Ensemble Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting

2022

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“…Due to humanity's growing technological advancements over the past century, solar eruptive events have emerged as a significant threat to society and its infrastructure. Electromagnetic radiation, solar energetic particles, and coronal mass ejections produced during solar flares have the potential to interfere with radio communications, GPS, and power grids (Natras et al 2019;Hudson 2021), which are crucial components to our everyday lives. Furthermore, these events pose considerable health risks to humans, in particular astronauts who are not shielded by Earth's magnetosphere and may receive increased doses of radiation.…”

Section: Introductionmentioning

confidence: 99%

Investigating Performance Trends of Simulated Real-time Solar Flare Predictions: The Impacts of Training Windows, Data Volumes, and the Solar Cycle

Goodwin,

Sadykov,

Martens

2024

ApJ

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This study explores the behavior of machine-learning-based flare forecasting models deployed in a simulated operational environment. Using Georgia State University’s Space Weather Analytics for Solar Flares benchmark data set, we examine the impacts of training methodology and the solar cycle on decision tree, support vector machine, and multilayer perceptron performance. We implement our classifiers using three temporal training windows: stationary, rolling, and expanding. The stationary window trains models using a single set of data available before the first forecasting instance, which remains constant throughout the solar cycle. The rolling window trains models using data from a constant time interval before the forecasting instance, which moves with the solar cycle. Finally, the expanding window trains models using all available data before the forecasting instance. For each window, a number of input features (1, 5, 10, 25, 50, and 120) and temporal sizes (5, 8, 11, 14, 17, and 20 months) were tested. To our surprise, we found that, for a window of 20 months, skill scores were comparable regardless of the window type, feature count, and classifier selected. Furthermore, reducing the size of this window only marginally decreased stationary and rolling window performance. This implies that, given enough data, a stationary window can be chosen over other window types, eliminating the need for model retraining. Finally, a moderately strong positive correlation was found to exist between a model’s false-positive rate and the solar X-ray background flux. This suggests that the solar cycle phase has a considerable influence on forecasting.

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“…In September 2017, a solar radio burst resulting from an X9.3 solar flare affected the GNSS positioning performance and navigation services in Europe (Berdermann et al, 2018). Also, a high number of solar flares of classes X and M in October 2014 produced enhanced ionization in the mid-latitude ionosphere over the southeast of Europe and degraded GNSS precise point positioning (Natras et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Multi-instrumental investigation of the solar flares impact on the ionosphere on 05–06 December 2006

Barta

Natraš

Srećković

et al. 2022

Front. Environ. Sci.

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The sudden increase of X-radiation and EUV emission following solar flares causes additional ionization and increased absorption of electromagnetic (EM) waves in the Earth’s atmosphere. The solar flare impact on the ionosphere above Europe on 05 and 06 December 2006 was investigated using ground-based (ionosonde and VLF) and satellite-based data (Vertical Total Electron Content (VTEC) derived from GNSS observations and VLF measurements from DEMETER satellite). Based on the Kp and Dst indices, 05 December 2006 was a quiet day, while there was a geomagnetic storm on 06 December 2006. The total fade-out of the EM waves emitted by the ionosondes was experienced at all investigated stations during an X9 class flare on 05 December 2006. The variation of the fmin parameter (first echo trace observed on ionograms, it is a rough measure of the “non-deviative” absorption) and its difference between the quiet period and during the flares have been analyzed. A latitude dependent enhancement of fmin (2–9 MHz) and Δfmin (relative change of about 150%–300%) was observed at every station at the time of the X9 (on 05 December) and M6 (on 06 December) flares. Furthermore, we analyzed VTEC changes during and after the flare events with respect to the mean VTEC values of reference quiet days. During the X9 solar flare, VTEC increased depending on the latitude (2–3 TECU and 5%–20%). On 06 December 2006, the geomagnetic storm increased ionization (5–10 TECU) representing a “positive” ionospheric storm. However, an additional peak in VTEC related to the M6 flare could not be detected. We have also observed a quantifiable change in transionospheric VLF absorption of signals from ground transmitters detected in low Earth orbit associated with the X9 and M6 flare events on 05 and 06 December in the DEMETER data. Moreover, amplitude and phase of ground-based, subionospherically propagating VLF signals were measured simultaneously during the investigated flares to analyze ionosphere reaction and to evaluate the electron density profile versus altitude. For the X9 and M6 flare events we have also calculated the ionospheric parameters (sharpness, reflection height) important for the description and modelling of this medium under forced additional ionization.

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Strong solar flare detection and its impact on ionospheric layers and on coordinates accuracy in the Western Balkans in October 2014

Cited by 7 publications

References 9 publications

Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting

Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting

Investigating Performance Trends of Simulated Real-time Solar Flare Predictions: The Impacts of Training Windows, Data Volumes, and the Solar Cycle

Multi-instrumental investigation of the solar flares impact on the ionosphere on 05–06 December 2006

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