Modelling Rainfall Prediction Using Data Mining Method - A Bayesian Approach

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Mr. Chetan C. Janbandhu, Prof. Praful D. Meshram, Prof. Madhuri N. Gedam

Abstract

Weather forecasting has been one of the most technically difficult problems around the globe. Weather data is meteorological data. It can be used for weather prediction. Weather data has 36 attributes but only 7 attributes are most important to rainfall prediction. Data is pre-processed to use it in this Bayesian approach. It is the data mining prediction model for rainfall prediction. The model is trained using the training data set and has been tested for accuracy on test data. The meteorological centres use high computing and supercomputing power to run weather prediction model. To address the problem of compute intensive rainfall prediction model, this paper studies data intensive model using data mining technique. This model works with efficient accuracy and uses moderate amount of compute resources for rainfall prediction. Bayesian approach is used for rainfall prediction. It works well with good accuracy.

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How to Cite
, M. C. C. J. P. P. D. M. P. M. N. G. (2017). Modelling Rainfall Prediction Using Data Mining Method - A Bayesian Approach. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 3(11), 472–474. Retrieved from http://www.ijfrcsce.org/index.php/ijfrcsce/article/view/333
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