Regionalization of Rainfall Using RCDA Cluster Ensemble Algorithm in India


The magnitude and frequency of precipitation is of great significance in the field of hydrologic and hydraulic design and has wide applications in varied areas. However, the availability of precipitation data is limited to a few areas, where the rain gauges are successfully and efficiently installed. The magnitude and frequency of precipitation in ungauged sites can be assessed by grouping areas with similar characteristics. The procedure of grouping of areas having similar behaviour is termed as Regionalization. In this paper, RCDA cluster ensemble algorithm is employed to identify the homogeneous regions of rainfall in India. Cluster ensemble methods are commonly used to enhance the quality of clustering by combining multiple clustering schemes to produce a more robust scheme delivering similar homogeneous regions. The goal is to identify, analyse and describe hydrologically similar regions using RCDA cluster ensemble algorithm. RCDA cluster ensemble algorithm, which is based on discriminant analysis. The algorithm takes H base clustering schemes each with K clusters, obtained by any clustering method, as input and constructs discriminant function for each one of them. Subsequently, all the data tuples are predicted using H discriminant functions for cluster membership. Tuples with consistent predictions are assigned to the clusters, while tuples with inconsistent predictions are analyzed further and either assigned to clusters or declared as noise. RCDA algorithm has been compared with Best of K-means and Clue cluster ensemble of R software using traditional clustering quality measures. Further, domain knowledge based comparison has also been performed. All the results are encouraging and indicate better regionalization of the rainfall in different parts of India.

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S. Ahuja and C. Dhanya, "Regionalization of Rainfall Using RCDA Cluster Ensemble Algorithm in India," Journal of Software Engineering and Applications, Vol. 5 No. 8, 2012, pp. 568-573. doi: 10.4236/jsea.2012.58065.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] P. Satyanarayana and V. V. Srinivas, “Regional Frequency Analysis of Precipitation Using Large-Scale Atmospheric Variables,” Journal of Geophysical Research, Vol. 113, No. D24, 2008. doi:10.1029/2008JD010412
[2] M. Rajeevan, J. Bhate and J. D. Kale, “A High Resolution Daily Gridded Rainfall Data for the Indian Region: Analysis of Break and Active Monsoon Spells,” Current Science, Vol. 91, No. 3, 2006, pp. 296-306.
[3] I. J. Jackson, “Inter-Station Rainfall Correlation under Tropical Conditions,” Catena 1, Elsevier, University of New England, Armidale, 1973, pp. 235-256.
[4] M. Bonell and G. Sumner, “Autumn and Winter Daily Precipitation Areas in Wales, 1982-1983 to 1986-1987,” International Journal of Climatology, Vol. 12, No. 1, 1992, pp. 77-102. doi:10.1002/joc.3370120108
[5] D. Sharon, “The Distribution in Space of Local Rainfall in the Namib Desert,” International Journal of Climatology, Vol. 1, No. 1, 1981, pp. 69-75. doi:10.1002/joc.3370010108
[6] H. S. Bedi and M. M. S. Bindra, “Principal Components of Monsoon Rainfall,” Tellus, Vol. 1, No. 32, 1980, pp. 296-298.
[7] K. K. Singh and S. V. Singh, “Space Time Variation and Regionalization of Seasonal and Monthly Summer Monsoon Rainfall of the Sub-Himalayan Region and Gangetic Plains of India,” Inter-Research Climate-Research, Vol. 6, 1996, pp. 251-262. doi:10.3354/cr006251
[8] D. R. Easterling, “Regionalization of Thunderstorm Rainfall in the Contiguous United States,” International Journal of Climatology, Vol. 9, No. 6, 1989, pp. 567-579. doi:10.1002/joc.3370090603
[9] Md. R. Ghaemi, H. I. N. Sulaiman and N. Mustapha, “A survey: Clustering Ensembles Techniques,” Proceedings of World Academy of Science, Engineering and Technology, Vol. 38, No. 2, 2002, pp. 2070-3740.
[10] X. Hu and I. Yoo, “Cluster Ensemble and Its Applications in Gene Expression Analysis,” Proceedings of 2nd Asia-Pacific Bioinformatics Conference, Dunedin, 18-22 January 2004, pp. 297-302.
[11] A. Topchy, A. B. Minaei-Bidgoli and W. F. Punch, “Adaptive Clustering Ensembles,” Proceedings of the 17th International Conference on Pattern Recognition, Vol. 1, 2004, pp. 272-275. doi:10.1109/ICPR.2004.1334105
[12] A. Topchy and W. Punch, “A Mixture Model for Clustering Ensembles,” Proceedings SIAM Conference on Data Mining, Nashville, 13-16 June 2004, pp. 379-390.
[13] M. D. Frossyniotis and A. Stafylopatis, “A Multi-Clustering Fusion Algorithm,” Methods and Applications of Artificial Intelligence, Springer, Berlin, Vol. 2, No. 2, 2002.
[14] B. Fischer and J. M. Buhmann, “Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 25, No. 4, 2003, pp. 513-518. doi:10.1109/TPAMI.2003.1190577
[15] A. Strehl and J. Ghosh, “Cluster Ensembles—A Knowledge Reuse Framework for Combining Multiple Partitions,” Journal of Machine Learning Research, Vol. 3, 2002, pp. 583-617.
[16] A. L. N. Fred, “Finding Consistent Cluster in Data Partitions,” Proceedings of 2nd International Workshop on Multiple Classifier Systems, Vol. 19, No. 9, 2001, pp. 309-318.
[17] A. L. N. Fred and A. K. Jain, “Data Clustering Using Evidence Accumulation,” Proceedings of International Conference on Pattern Recognition, Vol. 4 2002, pp. 276-280.
[18] A. Topchy, A. K. Jain and W. Punch, “Combining Multiple Weak Clusterings,” Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, 19- 22 November 2003, pp. 331-338. doi:10.1109/ICDM.2003.1250937
[19] V. Bhatnagar and S. Ahuja, “Robust Clustering Using Discriminant Analysis,” Proceedings of International Industrial Conference on Data Mining. Lecture Notes in Computer Science, Springer, Berlin, 2010, pp. 143-157.
[20] P. N. Tan, V. Kumar and M. Steinbach, “Introduction to Data Mining,” Pearson, Boston, 2006.
[21] L. Kaufman and P. Rousseeuw, “Finding Groups in Data an Introduction to Cluster Analysis,” Wiley Interscience, New York, 1990. doi:10.1002/9780470316801
[22] K. Hornik, 2012.

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