Application of support vector machine combined with K-nearest neighbors in solar flare and solar proton events forecasting

Li, Rong; Cui, Yanmei; He, Hongliang; Wang, Huaning

doi:10.1016/j.asr.2007.12.015

Cited by 48 publications

(33 citation statements)

References 8 publications

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“…This suggests that different classifier designs potentially offer complementary information. As the previous work has shown [39], [40], hybrid SVM/KNN classifier taking the advantages of the SVM and KNN, could be harnessed to improve the overall classifier performance.…”

Section: Discussionmentioning

confidence: 99%

Surface Roughness Detection of Arteries via Texture Analysis of Ultrasound Images for Early Diagnosis of Atherosclerosis

et al. 2013

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There is a strong research interest in identifying the surface roughness of the carotid arterial inner wall via texture analysis for early diagnosis of atherosclerosis. The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE−/− mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p<0.05). When the 11 optimal features were used as inputs to a support vector machine classifier, we achieved over 89% accuracy, 87% sensitivity and 93% specificity. The accuracy, sensitivity and specificity for the k-nearest neighbor classifier were 73%, 75% and 70%, respectively. The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall. This method is shown to be useful for early detection and diagnosis of atherosclerosis.

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Section: Discussionmentioning

confidence: 99%

Surface Roughness Detection of Arteries via Texture Analysis of Ultrasound Images for Early Diagnosis of Atherosclerosis

et al. 2013

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“…For instance, one can employ a time history of a given quantity, recorded with a certain time frequency. Examples of supervised regression in Space Weather are the forecast of a geomagnetic index, as function of solar wind parameters observed at L1 (Gleisner et al, ; Lundstedt & Wintoft, ; Macpherson et al, ; Uwamahoro & Habarulema, ; Valach et al, ; Weigel et al, ), the prediction of solar energetic particles (SEPs) (Fernandes, ; Gong et al, ; Li et al, ), of the F10.7 index for radio emissions (Ban et al, ; Huang et al, ), of ionospheric parameters (Chen et al, ), of sunspot numbers or, more in general, of the solar cycle (Ashmall & Moore, ; Calvo et al, ; Conway et al, ; Fessant et al, ; Lantos & Richard, ; Pesnell, ; Uwamahoro et al, ), of the arrival time of interplanetary shocks (Vandegriff et al, ), and of CMEs (Choi et al, ; Sudar et al, ).…”

Section: Machine Learning In Space Weathermentioning

confidence: 99%

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

2019

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The numerous recent breakthroughs in machine learning make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in Space Weather. The purpose is twofold. On one hand, we will discuss previous works that use machine learning for Space Weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the Space Weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray box. Plain Language SummaryIn the last decade, machine learning has achieved unforeseen results in industrial applications. In particular, the combination of massive data sets and computing with specialized processors (graphics processing units, or GPUs) can perform as well or better than humans in tasks like image classification and game playing. Space weather is a discipline that lives between academia and industry, given the relevant physical effects on satellites and power grids in a variety of applications, and the field therefore stands to benefit from the advances made in industrial applications. Today, machine learning poses both a challenge and an opportunity for the space weather community. The challenge is that the current data science revolution has not been fully embraced, possibly because space physicists remain skeptical of the gains achievable with machine learning. If the community can master the relevant technical skills, they should be able to appreciate what is possible within a few years time and what is possible within a decade. The clearest opportunity lies in creating space weather forecasting models that can respond in real time and that are built on both physics predictions and on observed data.

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“…The shape and complexity of sunspots in whitelight emission have been classified according to the sunspot growth level (McIntosh 1990). It is empirically known that larger sunspots with a large number of umbra and a more complicated magnetic flux structure tend to produce larger flares (e.g., Sammis et al 2000;Gallagher et al 2002;Li et al 2008;Colak & Qahwaji 2009;Bloomfield et al 2012;Lee et al 2012;Barnes et al 2016), as well as repeated flares in the same active regions (ARs) (e.g., Zirin 1988;Zirin & Marquette 1991).…”

Section: Introductionmentioning

confidence: 99%

“…To deal with the data, several machine-learning algorithms (see an introductory text to machine-learning, e.g., Hastie et al 2009) have been applied to the flare prediction problem: a neural network (Qahwaji & Colak 2007;Colak & Qahwaji 2009;Higgins et al 2011;Ahmed et al 2013), C4.5 decision trees (Yu et al 2009(Yu et al , 2010, learning vector quantization (Yu et al 2009;Rong et al 2011), a regression model (Lee et al 2007;Song et al 2009), k-nearest neighbor (Li et al 2008;Huang et al 2013;Winter et al 2015), a support vector machine (SVM) (Qahwaji & Colak 2007;Bobra & Couvidat 2015;Muranushi et al 2015), a relevant vector machine (AlGhraibah et al 2015), SVM regression , and an ensemble of four predictors (Guerra et al 2015). However, the best algorithm for flare prediction has not been discussed in previous works, and it cannot be found without directly comparing the performances of different algorithms.…”

Section: Introductionmentioning

confidence: 99%

Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms

Nishizuka¹,

Sugiura²,

Kubo³

et al. 2017

ApJ

152

135

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We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010-2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.

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Application of support vector machine combined with K-nearest neighbors in solar flare and solar proton events forecasting

Cited by 48 publications

References 8 publications

Surface Roughness Detection of Arteries via Texture Analysis of Ultrasound Images for Early Diagnosis of Atherosclerosis

Surface Roughness Detection of Arteries via Texture Analysis of Ultrasound Images for Early Diagnosis of Atherosclerosis

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms

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