SST clustering for winter precipitation prediction in southeast of Iran: Comparison between modified K-means and genetic algorithm-based clustering methods

Expert Systems with Applications

Volumn 38, Issue 5, 2011, Pages 5919-5929

  Zahraie, Banafsheh ^a   Roozbahani, Abbas ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

Author keywords

GA based clustering; K means clustering; Sea surface temperature (SST); Seasonal precipitation prediction

Indexed keywords

ARABIAN SEA; CLIMATIC VARIABLES; CLUSTERING METHODS; CLUSTERING TECHNIQUES; EUCLIDEAN DISTANCE; FITNESS FUNCTIONS; GA-BASED CLUSTERING; GEOGRAPHICAL ZONES; INDIAN OCEAN; INNOVATIVE APPROACHES; ISLAMIC REPUBLIC OF IRAN; K-MEANS; K-MEANS CLUSTERING; K-MEANS CLUSTERING METHOD; K-MEANS METHOD; NEW APPROACHES; PRECIPITATION DATA; PRECIPITATION VARIATION; RAIN GAUGES; SEA SURFACE TEMPERATURES; SEASONAL PRECIPITATIONS; STUDY AREAS; TEMPORAL CLASSIFICATION; TEMPORAL CLUSTERING; WINTER PRECIPITATION;

ATMOSPHERIC TEMPERATURE; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; FUNCTION EVALUATION; GAGES; GALLIUM ALLOYS; GENETIC ALGORITHMS; METEOROLOGICAL INSTRUMENTS; RAIN GAGES; SURFACE PROPERTIES; WEATHER FORECASTING;

RAIN;

EID: 79151471093     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.11.031     Document Type: Article

References (26)

1. C. Alippi, and R. Cucchiara Cluster partitioning in image analysis and classification: A genetic algorithm approach Proceedings of CompEuro 1992 on computer systems and software engineering 1992 IEEE Computer Society Press Silver Spring, MD 139 144
2. S. Bandyopadhyay, and U. Maulik Genetic clustering for automatic evolution of clusters and application to image classification Pattern Recognition 35 6 2002 1197 1208
3. Bhuyan, J. N.; Raghavan, V. V.; & Venkatesh, K. E. (1991). Genetic algorithm for clustering with an ordered representation. In Proceedings of the 4th international conference on genetic algorithms (pp. 408-415).
4. Y.C. Chiou, and L.W. Lan Theory and methodology genetic clustering algorithms European Journal of Operational Research 135 20 2001 413 427
5. I. Cordery, and M. McCall A model for forecasting droughts from teleconnections Water Resources Research 36 3 2000 763 768
6. M.C. Cowgill, R.J. Harvey, and L.T. Watson A genetic algorithm approach to cluster analysis Computers & Mathematics with Applications 37 7 1999 99 108
7. R. Cucchiara Analysis and comparison of different genetic models for the clustering problem in image analysis R.F. Albrecht, C.R. Reeves, N.C. Steele, Proceedings of the international conference on artificial neural networks and genetic algorithms 1993 Springer-Verlag 423 427
8. G. Garai, and B.B. Chaudhuri A novel genetic algorithm for automatic clustering Pattern Recognition Letters 25 2 2004 173 187
9. B. Harshburger, H. Ye, and J. Dzialoski Observational evidence of the influence of Pacific SSTs on winter precipitation and spring stream discharge in Idaho Journal of Hydrology 264 1-4 2002 157 169
10. R.W. Higgins, H.-K. Kim, and D. Unger Long-lead seasonal temperature and precipitation prediction using tropical pacific SST consolidation forecasts Journal of Climate 17 2004 3398 3414
11. E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, and L. Gandin The NCEP/NCAR reanalysis 40-year project Bulletin of the American Meteorological Society 77 1996 437 471
12. T. Kanungo, D.M. Mount, N.S. Netanyahu, C. Piatko, R. Silverman, and A.Y. Wu An efficient k-means clustering algorithm: Analysis and implementation IEEE Transactions on Pattern Analysis and Machine Intelligence 24 7 2002 881 892
13. M. Karamouz, and B. Zahraie Seasonal streamflow forecasting using snow budget and El-Nino Southern Oscillation climate signals: Application to the Salt River Basin in Arizona Journal of Hydrologic Engineering 9 6 2004 523 533
14. A. Likas, N. Vlassis, and J.J. Verbeek The global k-means clustering algorithm Pattern Recognition 36 2003 451 461
15. J.B. MacQueen Some methods for classification and analysis of multivariate observations Proceedings of 5th Berkeley symposium on mathematical statistics and probability Vol. 1 1967 University of California Press Berkeley 281 297
16. U. Maulik, and S. Bandyopadhyay Genetic algorithm-based clustering technique Pattern Recognition 33 9 2002 1455 1465
17. I. Moraczewski, W. Borkowski, and A. Kierzek Clustering geobotanical data with the use of a genetic algorithm COENOSES 10 1 1995 17 28
18. M.J. Nazemosadat Persian Gulf sea surface temperature as a drought diagnostic for southern parts of Iran Journal of Drought News Network 10 1998 12 14
19. M.J. Nazemosadat, and I. Cordery On the relationships between ENSO and autumn rainfall in Iran International Journal of Climatology 20 2000 47 61
20. Pelleg, D.; & Moore, A. (1999). Accelerating exact k-means algorithms with geometric reasoning. Technical Report CMU-CS-00105. Pittsburgh, PA: Carnegie Mellon University.
21. Pelleg, D.; & Moore, A. (2000). X-means: Extending k-means with efficient estimation of the number of clusters. In Proceedings of the seventeenth international conference on machine learning, Palo Alto, CA.
22. D. Pelleg, and A. Moore Cached sufficient statistics for efficient machine learning with large datasets Journal of Artificial Intelligence Research 8 1998 67 91
23. Y. Rucong, Z. Minghua, Y. Yongqiang, and L. Yimin Summer monsoon rainfalls over Mid-Eastern China lagged correlated with global SSTs Advances in Atmospheric Sciences 18 2 2001 179 196
24. T. Semmler, and D. Jacob Modeling extreme precipitation events - A climate change simulation for Europe Global and Planetary Change 44 1-4 2004 119 127
25. R. Xu, and D. Wunsch II Survey of clustering algorithms IEEE Transactions on Neural Networks 16 3 2005 645 678
26. Zahraie, B.; & Karamouz, M. (2004). Seasonal precipitation prediction using large scale climate signals. In Proceedings of EWRI-2004 conference, Salt Lake City, USA.