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Abstract
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Article Information:
Point Rainfall Prediction using Data Mining Technique
T.R. Sivaramakrishnan and S. Meganathan
Corresponding Author: S. Meganathan
Submitted: February 02, 2012
Accepted: March 02, 2012
Published: July 01, 2012 |
Abstract:
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Rainfall prediction is usually done for a region but spot quantitative precipitation forecast is required
for individual township, harbours and stations with vital installation. With recent successful attempt for
prediction of rainfall at a coastal station in east coast of India, a methodology to predict spot rainfall using
association rule mining for an interior station Trichirappalli (10º48' N/78º41' E) of south India has been
developed and the results are presented here. The data is filtered using discretization approach based on the best
fit ranges and then association mining is performed on dataset using Predictive Apriori algorithm and then the
data need be validated using K* classifier approach. The results show that the overall classification accuracy
for occurrence and non occurrence of the rainfall on wet and dry days using the data mining technique is
satisfactory.
Key words: Association rule mining, data mining, K* algorithm, predictive apriori algorithm, rainfall prediction, ,
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Abstract
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Cite this Reference:
T.R. Sivaramakrishnan and S. Meganathan, . Point Rainfall Prediction using Data Mining Technique. Research Journal of Applied Sciences, Engineering and Technology, (13): 1899-1902.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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