Long Range Forecast on South West Monsoon Rainfall using Artificial Neural Networks based on Clustering Approach

Long Range Forecast on South West Monsoon Rainfall using Artificial Neural Networks based on Clustering Approach

Автор: Maya L. Pai, Kalavampara V. Pramod, Alungal N. Balchand

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 7 Vol. 6, 2014 года.

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The purpose of this study is to forecast Southwest Indian Monsoon rainfall based on sea surface temperature, sea level pressure, humidity and zonal (u) and meridional (v) winds. With the aforementioned parameters given as input to an Artificial Neural Network (ANN), the rainfall within 10x10 grids of southwest Indian regions is predicted by means of one of the most efficient clustering methods, namely the Kohonen Self-Organizing Maps (SOM). The ANN is trained with input parameters spanning for 36 years (1960-1995) and tested and validated for a period of 9 years (1996-2004). It is further used to predict the rainfall for 6 years (2005-2010). The results show reasonably good accuracy for the summer monsoon periods June, July, August and September (JJAS) of the validation years.

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South West Monsoon, Clustering, Artificial Neural Networks, Self-Organizing Map

Короткий адрес: https://sciup.org/15012109

IDR: 15012109

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