Abstract Paper

Title	: A HYBRID MODEL OF K-MEANS CLUSTERING AND MULTI LAYER PERCEPTRON FOR RAINFALL PREDICTION
Author(s)	: R. Pugazendi, P. Usha
Article Information	: Volume 1 : Issue 2 (December - 2017) , 36 - 40
Affiliation(s)	: Assistant Professor, Dept. of Computer Science, Government Arts College, Periyar University, Salem – 7, Tamilnadu, India.
	: Research Scholar, Dept. of Computer Science, Government Arts College, Periyar University, Salem – 7, Tamilnadu, India.

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Abstract :

Abstract— India is a farming country which mostly depends on monsoon used for irrigation intention. An enormous sum of water is consumed for industrial creation, crop yield, and domestic use. Rainfall prediction is thus very essential and necessary for the development of the country. Weather factors including mean, precipitation, minimum temperature, average temperature, maximum temperature, cloud cover, vapor pressure, wet day frequency and diurnal temperature have been used to forecasts the rainfall and improve the accuracy of the rainfall prediction from the weather point. Continuous collection of historical weather data will assist in improving the accuracy and finding the rainfall prediction. The condition of weather data can be detected more accurately and in a fast manner, to terminate the problems and give a better solution. By using the hybrid methods K-Means clustering and Multilayer perceptron one can find the various issues and its consequences related to weather by analyzing and classifying the weather data there by producing an accurate rainfall prediction within a limited span of time and high-level accuracy.

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Keywords	: Rainfall Prediction, K-Means clustering, Multi Layer Perceptron (MLP), Indian Metrological Weather Data
Document Type	: Research Paper
Publication date	: January 06, 2018

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