Automated Solar Activity Prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares

Colak, Tufan; Qahwaji, Rami

doi:10.1029/2008sw000401

Cited by 156 publications

(90 citation statements)

References 12 publications

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“…The efforts made until now to forecast solar flares are often based on different conceptual procedures (see, e.g., Shaw 1989;Miller 1989;Moon et al 2001;Wheatland 2001;Gallagher et al 2002;Jensen et al 2004;Tobiska 2005;Wheatland 2005;Jing et al 2006;Ternullo et al 2006;Barnes et al 2007;Falconer et al 2007;Georgoulis and Rust 2007;Qahwaji and Colak 2007;Colak and Qahwaji 2009).…”

Section: Prediction Models For Solar Flaresmentioning

confidence: 99%

“…The system can provide a prediction up to six hours in advance by analysing the latest sunspot data, and it confirmed the direct relation between the flare production and certain McIntosh classes like Ekc, Fki and Fkc. The most recent development is ASAP (Automated Solar Activity Prediction), a hybrid computer platform using machine learning and solar imaging for automated prediction of significant solar flares (Colak and Qahwaji 2009; http://spaceweather.inf.brad.ac.uk/ asap.html). ASAP is an evolution of the prediction system by Qahwaji and Colak (2007).…”

Section: Prediction Models For Solar Flaresmentioning

confidence: 99%

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Solar Weather Event Modelling and Prediction

et al. 2009

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Key drivers of solar weather and mid-term solar weather are reviewed by considering a selection of relevant physics-and statistics-based scientific models as well as a selection of related prediction models, in order to provide an updated operational scenario for space weather applications. The characteristics and outcomes of the considered scientific and prediction models indicate that they only partially cope with the complex nature of solar activity for the lack of a detailed knowledge of the underlying physics. This is indicated by the fact that, on one hand, scientific models based on chaos theory and non-linear dynamics reproduce better the observed features, and, on the other hand, that prediction models based on statistics and artificial neural networks perform better. To date, the solar weather prediction success at most time and spatial scales is far from being satisfactory, but the forthcoming ground-and space-based high-resolution observations can add fundamental tiles to the modelling and predicting frameworks as well as the application of advanced mathematical approaches in the analysis of diachronic solar observations, that are a must to provide comprehensive and homogeneous data sets.

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Section: Prediction Models For Solar Flaresmentioning

confidence: 99%

Section: Prediction Models For Solar Flaresmentioning

confidence: 99%

Solar Weather Event Modelling and Prediction

et al. 2009

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“…This is the reason why those methods do not consider the evolution of a solar data time series in the mapping, which causes the loss of valuable information for the forecasting process. Some forecasting methods set solar data observed in a specific instant of time to the class of a solar flare occurred after the observed data [7][8][9]18,19]. However, there are also works that map subseries of the solar data into events observed in the future, so that they adequately consider the historical evolution of solar data [10,11].…”

Section: Related Workmentioning

confidence: 99%

SeMiner: A Flexible Sequence Miner Method to Forecast Solar Time Series

et al. 2018

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X-rays emitted by the Sun can damage electronic devices of spaceships, satellites, positioning systems and electricity distribution grids. Thus, the forecasting of solar X-rays is needed to warn organizations and mitigate undesirable effects. Traditional mining classification methods categorize observations into labels, and we aim to extend this approach to predict future X-ray levels. Therefore, we developed the "SeMiner" method, which allows the prediction of future events. "SeMiner" processes X-rays into sequences employing a new algorithm called "Series-to-Sequence" (SS). It employs a sliding window approach configured by a specialist. Then, the sequences are submitted to a classifier to generate a model that predicts X-ray levels. An optimized version of "SS" was also developed using parallelization techniques and Graphical Processing Units, in order to speed up the entire forecasting process. The obtained results indicate that "SeMiner" is well-suited to predict solar X-rays and solar flares within the defined time range. It reached more than 90% of accuracy for a 2-day forecast, and more than 80% of True Positive (TPR) and True Negative (TNR) rates predicting X-ray levels. It also reached an accuracy of 72.7%, with a TPR of 70.9% and TNR of 79.7% when predicting solar flares. Moreover, the optimized version of "SS" proved to be 4.36 faster than its initial version.

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“…ML techniques are by now ubiquitous in Astronomy, where they have been successfully applied to photometric redshift estimation in large surveys such as the Sloan Digital Sky Survey (Tagliaferri et al 2003;Li et al 2007;Ball et al 2007;Gerdes et al 2010;Singal et al 2011;Geach 2012;Carrasco Kind & Brunner 2013;Cavuoti et al 2014;Hoyle et al 2015a,b), automatic identification of quasi stellar objects (Yèche et al 2010), galaxy morphology classification (Banerji et al 2010;Shamir et al 2013;Kuminski et al 2014), detection of HI bubbles in the interstellar medium (Thilker et al 1998;Daigle et al 2003), classification of diffuse interstellar bands in the Milky Way (Baron et al 2015), prediction of solar flares (Colak & Qahwaji 2009;Yu et al 2009), automated classification of astronomical transients and detection of variability (Mahabal et al 2008;Djorgovski et al 2012;Brink et al 2013;du Buisson et al 2015;Wright et al 2015), cataloguing of impact craters on Mars (Stepinski et al 2009), prediction of galaxy halo occupancy in cosmological simulations (Xu et al 2013), dynamical mass measurement of galaxy clusters (Ntampaka et al 2015), and supernova identification in supernova searches (Bailey et al 2007). Software tools developed specifically for astronomy are also becoming available to the community, still mainly with large observational datasets in mind (VanderPlas et al 2012;Vander Plas et al 2014;VanderPlas et al 2014;Ball & Gray 2014).…”

Section: Introductionmentioning

confidence: 99%

Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

Pasquato

Chung

2016

A&A

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Context. Machine-learning (ML) solves problems by learning patterns from data with limited or no human guidance. In astronomy, ML is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We apply ML to gravitational N-body simulations of star clusters that are either formed by merging two progenitors or evolved in isolation, planning to later identify globular clusters (GCs) that may have a history of merging from observational data. Methods. We create mock-observations from simulated GCs, from which we measure a set of parameters (also called features in the machine-learning field). After carrying out dimensionality reduction on the feature space, the resulting datapoints are fed in to various classification algorithms. Using repeated random subsampling validation, we check whether the groups identified by the algorithms correspond to the underlying physical distinction between mergers and monolithically evolved simulations.Results. The three algorithms we considered (C5.0 trees, k-nearest neighbour, and support-vector machines) all achieve a test misclassification rate of about 10% without parameter tuning, with support-vector machines slightly outperforming the others. The first principal component of feature space correlates with cluster concentration. If we exclude it from the regression, the performance of the algorithms is only slightly reduced.

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Automated Solar Activity Prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares

Cited by 156 publications

References 12 publications

Solar Weather Event Modelling and Prediction

Solar Weather Event Modelling and Prediction

SeMiner: A Flexible Sequence Miner Method to Forecast Solar Time Series

Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

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