Author(s): Sivajothi R, K. Karthikeyan

Email(s): kkarthikeyan67@yahoo.co.in

DOI: 10.5958/0974-360X.2016.00287.0   

Address: Sivajothi R1, K. Karthikeyan2*
1Research Scholar, School of Advanced Sciences, VIT-University, Vellore, Tamil Nadu, India.
2Associate Professor, School of Advanced Sciences, VIT-University, Vellore, Tamil Nadu, India.
*Corresponding Author

Published In:   Volume - 9,      Issue - 9,     Year - 2016


ABSTRACT:
The Gamma distribution model is well en suite to the monthly and annual rainfall estimate detection. The rainfall gauging station of Pampadumpara, Idukki district metrological station data is being used. This study tests the goodness-of-fit using the Kolmogorov-simirnov (KS) test, and compare these results against gamma distribution which is commonly used during rainfall events. This distribution makes it feasible to estimate the likelihood of rainfall being within the specified range. In this paper, we consider the application of Gamma distribution in modelling intense rainfall. The distribution was applied to the monthly rainfall data from Pampadumpara station with the observation period from January 1978 to December 2013. The figures showed that the annual daily maximum rainfall received at any time ranged between 0.0 mm (Min) to 775.5 mm (Max) indicating a diverse range of fluctuation during the period of study. For this set of figures, Gamma distribution is opted for a better performance. The scientific results clearly established that the analytical procedure devised and tested in this study may be suitably applied for the identification of the best fit probability distribution of weather parameters. The distribution level is very good to predict the maximum monthly rainfall. The results showed that the Gamma distribution is very appropriate for extreme monthly rainfall. These models are used as decision making tools for food security, water management, agriculture, and hydroelectric power providing decision.


Cite this article:
Sivajothi R, K. Karthikeyan. Analysis of Monthly Rainfall Data Prediction for Change of Economic Environment in Pampadumpara Using Gamma distribution. Research J. Pharm. and Tech 2016; 9(9):1477-1482. doi: 10.5958/0974-360X.2016.00287.0


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DOI: 10.5958/0974-360X 

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