JOURNAL OF COMPUTER AND MATHEMATICAL SCIENCES An International Open Free Access, Peer Reviewed Research Journal www.compmath-journal.org
ISSN 0976-5727 (Print) ISSN 2319-8133 (Online) Abbr:J.Comp.&Math.Sci. 2014, Vol.5(3): Pg.328-331
Relationship between Interplanetary Parameters and CRI using DM Technique Shailendra Singh1, Ajay K. Pandey2,Navita Shrivastava1and R. K. Tiwari2 1
A.P.S. University, Rewa, M.P., INDIA. 2 Govt. New Science College, Rewa, M. P., INDIA. (Received on: June 27, 2014) ABSTRACT The main aim of this paper is to apply data mining technique for geomagnetic, interplanetary and cosmic ray data. We used clustering technique on solar and interplanetary data to explore the hidden patterns inside the largest data set. This technique helps to transfer the retrieved information into usable knowledge for classification and prediction of interplanetary condition and compare our finding to the existing results. The data volumes have been divide into 5 clusters and their centroid have been fixed. We have performed the statistical analysis between the various data sets. The cross plot between Ap versus B, V and product V.B show strong correlation and the cross plot between CRI versus V, B and product V.B show high anti-correlation (R2> 0.95) which supports earlier studies. Keywords: Clustering, k-means, Data Mining, CRI, Geomagnetic Activity.
INTRODUCTION The data mining technique is used extensively for extracting the accurate knowledge in developing models for prediction of climate. It will open a new era in the field of data mining and climate
prediction. The solar terrestrial relationships manifest the large scale disturbances near earth and in the interplanetary space. Many researchers1-4 have given relationships for long period between geomagnetic disturbance and interplanetary parameters. They have tried to use data mining
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Shailendra Singh, et al., J. Comp. & Math. Sci. Vol.5 (3), 328-331 (2014)
technologies in areas related to metrology and climate prediction. Dwivedi et al.5 has been carried out the cluster analysis among Ap, V, B and V.B and they indicated that neither V nor B is significantly effective in producing geomagnetic disturbances as compare to V and B together. The geomagnetic activity is mainly controlled by variations in the solar wind and interplanetary magnetic field (IMF) features. The solar wind data available during 19652013 has been utilised to study the large scale features in interplanetary medium and their effect on the geomagnetic activity. The solar wind plasma speed V, Interplanetary magnetic field B, V.B and geomagnetic activity are anti-correlated with cosmic ray intensity. Using the experimental data of the high counting rate neutron monitors, the solar variation of galactic cosmic radiation has been investigated for the period 1965– 2013. We found that geomagnetic Index Ap is well correlated with V B and V.B
the various data sets. The correlation between them has been calculated. The k-means algorithm takes the input parameter, k and partitions set of an object into k-cluster so that the resulting intra cluster similarity is high but the inter cluster similarity is low. Cluster similarities are measured in regard to the mean value of objects in a cluster, which can be viewed as the cluster's centroid or centre of gravity6-7. The k-means algorithm for partitioning is used in which each cluster's centre is represented by mean value of the object in the cluster. The number of clusters k and data set containing n objects (D) are taken as input. A set of k clusters is the output. We have arbitrarily chosen k objects from D as the initial cluster centres and reassign each object to the cluster to which the object is the most similar, based on the mean value of the object in the cluster. The process is repeated for updating the cluster mean until no change.
METHODOLOGY
RESULT AND DISCUSSION
The data set for the geomagnetic parameter Ap, interplanetary parameter V, B and geomagnetic index Ap have been collected from the omini data web (www.ominiweb.gsfc.nasa.gov) for the period 1965-2013, covering solar cycle 2024. The data for the cosmic ray intensity have been taken for the Kiel neutron monitoring station, for the same period. We have used the clustering method for our data after applying the data cleaning and other data mining technique. We have removed the outliers in the data set. The data volumes have been divide into 5 clusters and their centroid have been fixed. Then we have performed the statistical analysis between
The statistical analysis is performed among the different clusters on the data sets for geomagnetic activity and IMF parameters along with cosmic ray intensity. The cross plot between Ap versus B, V and product V.B is shown in figure 1(a,b,c). It is observe that the parameters are strongly correlated. The cross plot between CRI versus V, B and product V.B show high anticorrelation (R2>0.95) as shown in the figure 2(a,b,c). Our findings support the work done by Dwivedi et al 2010. They pointed out that the product V.B is a very good indicator for the large scale geomagnetic disturbances. The application of k-means method and the neural networks for the climate data will be
Journal of Computer and Mathematical Sciences Vol. 5, Issue 3, 30 June, 2014 Pages (258-331)
Shailendra Singh, et al., J. Comp. & Math. Sci. Vol.5 (3), 340-343 (2014)
implemented in future for modelling and
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prediction.
Table-1: The centoried for geomagnetic activity and interplanetary parameters along with CRI B
V
V.B
Ap
CRI
Centroid 1
8.463
512.3
4177.91
21.48
6008
Centroid 2
6.3
444.6
2725.87
11.07
6105
Centroid 3
12.4
540.4
6523.31
39.32
5849
Centroid 4
19.88
671.3
13058.25
96.19
5485
Centroid 5
4.234
375.8
1581.03
5.001
6329
Figure 1(a,b,c): The cross plot between Ap and B,V, V.B values for the period 1965-2013.
Figure 2(a,b,c): The cross plot between CRI and B,V, V.B values for the period 1965-2013. Journal of Computer and Mathematical Sciences Vol. 5, Issue 3, 30 June, 2014 Pages (258-331)
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Shailendra Singh, et al., J. Comp. & Math. Sci. Vol.5 (3), 328-331 (2014)
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4. Kane, R.P. J Geophys Res. 108, A1046, (2003). 5. Dwivedi V.C, Pandey V.S., Tiwari D.P. &Agrawal S.P. J Geophys Res. 39, 252, (2010). 6. Kalyankar M.A. and Alaspurkar S.J. IJRCSSE, 3(2) (2013). 7. Badhiye S.S., Wakode B.V Chatur P.N. IJCSIT 3(1), 3012, (2012).
Journal of Computer and Mathematical Sciences Vol. 5, Issue 3, 30 June, 2014 Pages (258-331)