Prediction of Rainfall Using Unsupervised Model based Approach Using K-Means Algorithm
Автор: G.Vamsi Krishna
Журнал: International Journal of Mathematical Sciences and Computing(IJMSC) @ijmsc
Статья в выпуске: 1, 2015 года.
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Prediction of rainfall has gained a significant importance because of many associated factors like cultivating, aqua-culture and other indirect parameters allied with the rainfall like global heat. Therefore it is necessary to predict the rainfall from the satellite images effectively. In this article, a segmentation algorithm is developed based on Gaussian mixture models. The initial parameters are estimated using k-means algorithm. The process is presented by using an 2-fold architecture, where in the first stage database creation is considered and the second stage talks about the prediction. The performance analysis is carried out using metrics like PSNR, IF and MSE. The developed model analyzes the satellite images and predicts the Rainfall efficiently.
Rainfall prediction, Gaussian mixture model, K-Means algorithm, rainfall estimation, PSNR, MSE
Короткий адрес: https://sciup.org/15010114
IDR: 15010114
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