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Nordic Hydrology Vol 38 No 3 pp 303–314 © IWA Publishing 2007 doi:10.2166/nh.2007.013

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

K. Srinivasa Raju1 and D. Nagesh Kumar2

1Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, 333 031, India
2Department of Civil Engineering, Indian Institute of Science, Bangalore, 560 012, India


ABSTRACT

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies–Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

Keywords: Cluster analysis; Davies–Bouldin index; fuzzy cluster analysis; India; Kohonen artificial neural networks; meteorological stations


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