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Identifying Rainfall Diversity through Clustering

Vikas Singh & Mukesh Kumar

International Journal of Ecological Economics and Statistics2021article
AJG 1
Weight
0.26

What the paper says

The uneven distribution of rainfall at different regions is an important issue now days. Particularly the places of low rainfall are of serious concerns. These places are lacking in drinking water and lot of other related problems like unsatisfactory irrigation, sanitation, etc. The rainfall diversity in India through clusters (similar regions) has been observed under this study. The rainfall distribution in India is varied over seasons, and high-rainfall observed under monsoon season. In this paper, we have analyzed rainfall data in the context of India through divisive clustering technique K-means and model based probability clustering. The techniques are used over rainfall data given by the Indian Meteorology Department (IMD), and a comparative finding has been reported. The annual and seasonal rainfalls data for the year 2016-17 has been taken for this study. Optimal numbers of clusters are obtained by using the Elbow method in case of K-means clustering and Bayesian Information Criterion (BIC) is used for model based clustering. Moreover, the results obtained through K-means and model based cluster analysis have been reported in terms of spatial distribution. The main advantage of splitting the states of rainfall into homogenous groups based on similar features is that it eliminates the necessity to carry out a detailed drought characteristic analysis for any location of interest. Therefore detection of rainfall pattern in a spatial domain is very important and useful for policy makers, climatologists, government, hydrologists and local and regional planners and managers.

Cite this paper

@article{vikas2021,
  title        = {{Identifying Rainfall Diversity through Clustering}},
  author       = {Vikas Singh & Mukesh Kumar},
  journal      = {International Journal of Ecological Economics and Statistics},
  year         = {2021},
}

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Identifying Rainfall Diversity through Clustering

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Evidence weight

0.26

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.20 × 0.15 = 0.03
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.