Share This Article:

Regionalization of River Basins Using Cluster Ensemble

Abstract Full-Text HTML Download Download as PDF (Size:632KB) PP. 560-566
DOI: 10.4236/jwarp.2012.47065    3,661 Downloads   6,031 Views   Citations
Author(s)    Leave a comment

ABSTRACT

In the wake of global water scarcity, forecasting of water quantity and quality, regionalization of river basins has attracted serious attention of the hydrology researchers. It has become an important area of research to enhance the quality of prediction of yield in river basins. In this paper, we analyzed the data of Godavari basin, and regionalize it using a cluster ensemble method. 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 basins. The goal is to identify, analyse and describe hydrologically similar catchments using cluster analysis. Clustering has been done using 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. Clustering results of RCDA algorithm have 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 Godavari basin data.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

S. Ahuja, "Regionalization of River Basins Using Cluster Ensemble," Journal of Water Resource and Protection, Vol. 4 No. 7, 2012, pp. 560-566. doi: 10.4236/jwarp.2012.47065.

References

[1] P.-S. Yu, H.-P. Tsai, S.-T. Chen and Y.-C. Wang, “Estimation of Design Flow in Ungauged Basins by Regionalization,” Department of Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan, 2005.
[2] Y. Zhang and F. Chiew, “Evaluation of Regionalization Methods for Predicting Runoff in Ungauged Catchments in Southeast Australia,” CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water 13-1, 2009.
[3] R. Ley, M. C. Casper, H. Hellebrand and R. Merz, “Catchment Classification by Runoff Behaviour with Self-Organizing Maps (SOM),” Journal of Hydrology and Earth System Sciences, Vol. 15, 2011, pp. 2947-2962. doi:10.5194/hess-15-2947-2011
[4] G. Busch, J. Sutmoller and G. Gerold, “Regionalization of Runoff Information by Aggregation of Hydrological Response Units: A Regional Comparison,” Proceedings of a Conference Regionalization in Hydrology, Vol. 254, 1997.
[5] R. Ghaemi, N. Sulaiman, H. Irahim 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.
[6] X. Hu and I. Yoo, “Cluster Ensemble and Its Applications in Gene Expression Analysis,” Proceedings of Second Asia-Pacific Bioinformatics Conference, Vol. 29, 2004, pp. 297-302.
[7] A. Topchy, B. Minaei-Bidgoli, A. K. Jain 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
[8] A. Topchy, A. K. Jain and W. Punch, “A Mixture Model for Clustering Ensembles,” Proceedings SIAM Conference on Data Mining, 2004, pp. 379-390.
[9] M. D. Frossyniotis and A. Stafylopatis, “A Multi-Clustering Fusion Algorithm,” SETN’02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence, Springer, London, 2002.
[10] 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
[11] 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.
[12] A. L. N. Fred, “Finding Consistent Cluster in Data Partitions,” Proceedings of 2nd International Workshop on Multiple Classifier Systems, Vol. 2096, 2001, pp. 309-318.
[13] 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.
[14] A. Topchy, A. K. Jain and W. Punch, “Combining Multiple Weak Clusterings,” Proceedings of the 3rd IEEE International Conference on Data Mining, 19-22 November 2003, pp. 331-338. doi:10.1109/ICDM.2003.1250937
[15] V. Bhatnagar and S. Ahuja, “Robust Clustering Using Discriminant Analysis,” Proceedings of International Industrial Conference on Data Mining, Vol. 6171, 2010, pp. 143-157.
[16] P. N. Tan and V. Kumar and M. Steinbach, “Introduction to Data Mining,” Pearson, March 2006.
[17] http://cran.r-project.org/web/packages/clue/clue.pdf

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.