Autom ated Prediction of Solar Flares Using Neural Netw orks and - - PowerPoint PPT Presentation

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Tufan Colak t.colak@bradford.ac.uk & Rami Qahwaji r.s.r.qahwaji@bradford.ac.uk

EIMC, University of Bradford BD71DP, U.K. Autom ated Prediction of Solar Flares Using Neural Netw orks and Sunspots Associations

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Organisation of this talk

Objectives & related work Solar data (features and activities) Data Association Machine learning algorithms Practical results Conclusions and future work

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Objective:

We aim to design an automated system that could provide short-term prediction of solar flares by establishing a correlation between sunspots and solar flares using machine learning.

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Related Work

Despite the recent advances in solar imaging, machine learning has not been widely applied to solar data, except for verification purposes. Solar activity (i.e., Wolf Number) was predicted first by (Calvo et al. 1995). (Borda et al. 2002) described a method for the automatic detection of solar flares using BP MLP. MLP, SVM and RBF were used for flares detection in (Qu et al. 2003).

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Organisation of this talk

Objectives & related work

Solar data (features and activities)

Data Association Machine learning algorithms Practical results Conclusions and future work

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Data?

Data from the publicly available National Geophysical Data Centre (NGDC) sunspot groups and flares catalogues are used in

• ur study.

NGDC keeps record of data from several

• bservatories around the world and holds
• ne of the most comprehensive publicly

available databases for solar features and activities.

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The NGDC sunspots catalogue

The NGDC sunspot catalogue holds records of sunspot groups supplying their date, time, location, physical properties, sunspot area and classification data. Two classification systems exist for sunspots: McIntosh, which depends on the size, shape and spot density of sunspots, and Mt. Wilson., which is based on the distribution of magnetic polarities within spot groups.

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The NGDC Flares catalogue

This catalogue provides information about dates, starting and ending times for flare eruptions, location, NOAA number of the corresponding active region and x-ray classification for the detected flares. Not all the flares have associated NOAA numbers. Flares without NOAA numbers are not included in

• ur study.

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Data

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Organisation of this talk

Objectives & related work Solar data (features and activities)

Data Association and prediction model

Machine learning algorithms Practical results Conclusions and future work

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We’ve investigated all the sunspot groups that were associated with flares from 01 Jan1992 till 31 Dec 2005. The degree of association was determined based on the NOAA region number and the timing information.

A C++ platform that extracts online flares and sunspots info from NGDC catalogues was created.

Our software has analysed the data related to 29343 flares and 110241 sunspots and has managed to associate 1425 M and X flares with their corresponding sunspot groups.

Associating Flares and Sunspots

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Associating Flares and Sunspots

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The Theoretical Model

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Organisation of this talk

Objectives & related work Solar data (features and activities) Data Association

Machine learning algorithms

Practical results Conclusions and future work

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Cascade FFBP

In cascade FFBP, the first layer has connecting weights with the input layer. Each subsequent layer has weights connecting it to the input layer and all previous layers. .

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Optimising the Learning Algorithms

• A learning algorithm provides best generalisation if it is optimised.
• A NN is optimised if the optimum topology, learning algorithm and

learning times are found.

• After finding that CCNN provides best performance, the number of

hidden nodes was found empirically.

• We have started with 1 hidden node and continuously were increasing

the number of hidden nodes until 35 hidden nodes were reached.

• Every time a new number of hidden nodes were used, the error rate

and the recognition rate were recorded. After carrying out all the empirical experiments it was found that optimum performance was reached with 9 hidden nodes.

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Organisation of this talk

Objectives & related work Solar data (features and activities) Data Association

Machine learning algorithms

Practical results Conclusions and future work

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Both NGDC catalogues were used and our software has analysed the data related to 29343 flares and 110241 sunspots and has managed to associate 1425 M and X flares with their corresponding sunspot groups. The total number of samples used for our training set is 2882, where 1425 samples represent sunspots that produced flares. The remaining samples represent sunspots that existed in non-flaring days and are not related to any sunspot groups within the previous flaring sunspot samples.

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The Training and Testing Sets

The NN training and testing was carried out based on the statistical Jack-knife technique (Fukunaga 1990).

For all the experiments, 80% of the samples are randomly selected and used for training while the remaining 20% are used for testing. These experiments are repeated for number of times and the average is taken.

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Initial Experiments

For each sample, the training vector consists of 5 elements ( 3 for inputs; 2 for outputs).

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Initial Experiments

Several experiments based on the Jack-knife technique were carried out and we found that the prediction rate for flares in the best case scenario was 72.9%. This indicated that a correlation existed between the input and output sets. But this value is not high enough to provide reliable prediction of solar activities. To improve the learning performance we tried to associate the classified sunspots with the sunspot cycle.

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This seemed logical because the rise and fall of solar activity coincides with the sunspot cycle (Pap et al. 1990). When the solar cycle is at a maximum, plenty of large active regions exist and many solar flares are

• detected. These decreases in number as the Sun

approaches the minimum part of its cycle (Pap et

• al. 1990).

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Solar Cycle and Flares

Science @ NASA,"Solar Minimum Explodes", 9.15.2005 WSC11 -(Sep 25-Oct 6)

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Solar Cycle Modelling-Hathaway’s Model

a represents the amplitude and is related to the rise of the cycle minimum, b is related to the time in months from minimum to maximum; c gives the asymmetry of the cycle; and to denotes the starting time

Average Monthly Sunspot Number

20 40 60 80 100 120 140 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Years Number of Sunspots

Generated Real

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For each sample, the training vector consists of 6 elements ( 4 for inputs; 2 for outputs).

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Hence, for Fkc sunspot at solar maximum that produced an M flare, the training vector looks like this:

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Organisation of this talk

Objectives & related work Solar data (features and activities) Data Association Machine learning algorithms Practical results

Conclusions and future work

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Conclusions

A fully automated computer platform that could verify this correlation between sunspot classes and solar flares relation using machine learning, is designed. The association and learning softwares will become public shortly at

• Our findings show that there is a direct relation between the

eruptions of flares and certain McIntosh classes of sunspots such as Ekc, Fki and Fkc. Our findings are in accordance with (McIntosh 1990), (Warwick 1966), and (Sakurai 1970). http: / / spaceweather.inf.bradford.ac.uk/

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X-ray Flares versus McIntosh Classification

0.0 5.0 10.0 15.0 20.0 25.0 CAI CAO CHO CKO CRO CSI CSO DAC DAI DAO DHC DHI DHO DKC DKI DKO DSI DSO EAC EAI EAO EHC EHO EKC EKI EKO ESI ESO FAC FAI FAO FHC FHI FHO FKC FKI FKO FSO McIntosh Class Percentage

X Class Flare M Class Flare

X-ray Flares versus Mt. Wilson Classification

5 10 15 20 25 30 35 40 45 50 B BD BG BGD G GD

• Mt. Wilson Class

P e r c e n ta g e M Class Flare X Class Flare

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Future Work

Apply image segmentation and classification algorithms to detect sunspots and classify them automatically, so that the platform is completed. To track the individual sunspot groups over their lifetime. The development of the sunspot group can contribute to the knowledge of the machine learning systems. Will better prediction be achieved if the magnetic configuration of sunspots (Mt. Wilson classification) is combined with the sunspot area to replace the McIntosh classification (Sammis, Tang & Zirin, 2000, ApJ)?

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To compare our findings with other authors who tested the correlations of the various McIntosh classes on flare rates and the applications to solar flare prediction (e.g. McIntosh 1990; Bornmann & Shaw 1994, Sol. Phys. 150, p. 127; Gallagher et al. 2002, Sol. Phys. 209, p. 171; Wheatland 2004, ApJ 609, p. 1134).

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• Acknowledgment. This work is supported by an

EPSRC Grant (GR/T17588/01), which is entitled “Image Processing and Machine Learning Techniques for Short-Term Prediction of Solar Activity”.

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