Solar Flare Forecasting from Magnetic Feature Properties Generated by the Solar Monitor Active Region Tracker

Domijan, Katarina; Bloomfield, D. Shaun; Pitié, François

doi:10.1007/s11207-018-1392-4

Cited by 24 publications

(17 citation statements)

References 44 publications

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“…Comparative analyses of algorithms have been used in many different application domains, see for example [31]. The success of potential approaches is domain specific and will depend on the structure of the data at hand.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Machine Learning Models in Food Authentication Studies

Singh

Domijan

2019

2019 30th Irish Signals and Systems Conference (ISSC)

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The underlying objective of food authentication studies is to determine whether unknown food samples have been correctly labelled. In this paper we study three near infrared (NIR) spectroscopic datasets from food samples of different types: meat samples (labelled by species), olive oil samples (labelled by their geographic origin) and honey samples (labelled as pure or adulterated by different adulterants). We apply and compare a large number of classification, dimension reduction and variable selection approaches to these datasets. NIR data pose specific challenges to classification and variable selection: the datasets are high -dimensional where the number of cases (n) << number of features (p) and the recorded features are highly serially correlated. In this paper we carry out comparative analysis of different approaches and find that partial least squares, a classic tool employed for these types of data, outperforms all the other approaches considered.

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Section: Discussionmentioning

confidence: 99%

Comparison of Machine Learning Models in Food Authentication Studies

Singh

Domijan

2019

2019 30th Irish Signals and Systems Conference (ISSC)

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“…Because of the random nature of solar flares, it is difficult to find an effective precursor. In the recent development of solar flare forecast, deep learning methods [76][77][78][79] are used to automatically extract forecasting patterns from the observational data and finally build a forecasting model. Depending on the big observational solar data, deep learning methods may be one of ways to improve Figure 4 illustrates the structure of the CNN.…”

Section: Solar Flare Forecasting Modelsmentioning

confidence: 99%

Space Weather Related to Solar Eruptions With the ASO-S Mission

et al. 2020

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The Advanced Space-based Solar Observatory (ASO-S) is a mission aiming at exploring solar flares, coronal mass ejections (CMEs), solar magnetic field and their relationships. To fulfill its major scientific objectives, ASO-S has three elaborately-designed payloads onboard: the Full-disk vector MagnetoGraph (FMG), the Lyman-alpha Solar Telescope (LST), and the Hard X-ray Imager (HXI) dedicated to observe vector magnetic fields, CMEs, and flares, respectively. Beside the scientific objectives, we have an operational objective to observe solar eruptions and magnetic field for making related space weather forecasts. More specifically, we have set a priority for the downlink of CME data observed by LST, and will distribute those data to different space weather prediction centers in China within 2 h once the Science Operation and Data Center (SODC) of ASO-S receive the data. After data downlink and archiving, different automatic detection, tracking, and cataloging procedures are planned to run for the most critical solar eruptive features. On the other hand, based on the distributed and downloaded data, different space weather prediction centers are to activate their forecast systems for the ASO-S observed solar eruption events. Our particular interests are currently focused on nowcast of different eruption events, prediction of CME arrivals, forecast of solar flares and the onset of solar eruptions. We are also working on further forecast potentials using the ASO-S data to make contributions to other possible important issues of space weather.

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“…Recently, the application of supervised machine learning methods, especially deep neural networks (DNNs), to solar flare prediction has been a hot topic, and their successful application in research has been reported (Huang et al 2018;Nishizuka et al 2018;Park et al 2018;Chen et al 2019;Domijan et al 2019;Liu et al 2019;Zheng et al 2019;Bhattacharjee et al 2020;Jiao et al 2020;Li et al 2020;Panos & Kleint 2020;Yi et al 2020). However, there is insufficient discussion on how to develop the methods available to real-time operations in space weather forecasting offices, including the methods for validation and verification of the models.…”

Section: Introductionmentioning

confidence: 99%

Operational solar flare prediction model using Deep Flare Net

Nishizuka

Kubo

Sugiura

et al. 2021

Earth Planets Space

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We developed an operational solar flare prediction model using deep neural networks, named Deep Flare Net (DeFN). DeFN can issue probabilistic forecasts of solar flares in two categories, such as ≥ M-class and < M-class events or ≥ C-class and < C-class events, occurring in the next 24 h after observations and the maximum class of flares occurring in the next 24 h. DeFN is set to run every 6 h and has been operated since January 2019. The input database of solar observation images taken by the Solar Dynamic Observatory (SDO) is downloaded from the data archive operated by the Joint Science Operations Center (JSOC) of Stanford University. Active regions are automatically detected from magnetograms, and 79 features are extracted from each region nearly in real time using multiwavelength observation data. Flare labels are attached to the feature database, and then, the database is standardized and input into DeFN for prediction. DeFN was pretrained using the datasets obtained from 2010 to 2015. The model was evaluated with the skill score of the true skill statistics (TSS) and achieved predictions with TSS = 0.80 for ≥ M-class flares and TSS = 0.63 for ≥ C-class flares. For comparison, we evaluated the operationally forecast results from January 2019 to June 2020. We found that operational DeFN forecasts achieved TSS = 0.70 (0.84) for ≥ C-class flares with the probability threshold of 50 (40)%, although there were very few M-class flares during this period and we should continue monitoring the results for a longer time. Here, we adopted a chronological split to divide the database into two for training and testing. The chronological split appears suitable for evaluating operational models. Furthermore, we proposed the use of time-series cross-validation. The procedure achieved TSS = 0.70 for ≥ M-class flares and 0.59 for ≥ C-class flares using the datasets obtained from 2010 to 2017. Finally, we discuss the standard evaluation methods for operational forecasting models, such as the preparation of observation, training, and testing datasets, and the selection of verification metrics.

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Solar Flare Forecasting from Magnetic Feature Properties Generated by the Solar Monitor Active Region Tracker

Cited by 24 publications

References 44 publications

Comparison of Machine Learning Models in Food Authentication Studies

Comparison of Machine Learning Models in Food Authentication Studies

Space Weather Related to Solar Eruptions With the ASO-S Mission

Operational solar flare prediction model using Deep Flare Net

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