Maya L. Pai; Kalavampara V. Pramod; Alungal N. Balchand

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

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Author(s)

Maya L. Pai ^1,* Kalavampara V. Pramod ² Alungal N. Balchand ³

1. Department of Mathematics, Amrita Vishwa Vidyapeetham, Cochin, 682024, India

2. Department of Computer Applications, CUSAT, Cochin, 682022, India

3. Department of Physical Oceanography, CUSAT, Cochin, 682016, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2014.07.01

Received: 7 Nov. 2013 / Revised: 10 Mar. 2014 / Accepted: 2 May 2014 / Published: 8 Jun. 2014

Index Terms

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

Abstract

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.

Cite This Paper

Maya L. Pai, Kalavampara V. Pramod, Alungal N. Balchand, "Long Range Forecast on South West Monsoon Rainfall using Artificial Neural Networks based on Clustering Approach", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.7, pp.1-8, 2014. DOI:10.5815/ijitcs.2014.07.01

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