Application of Artificial Neural Networks for monsoon rainfall prediction

Prediction of monsoon rainfall in a timely manner can be highly beneficial for Pakistan, where monsoon is the major source of rain. Presently, Multiple Linear Regression and Statistical Downscaling Models are being used for monsoon rainfall prediction. In spite of making use of a large number of resources and having dependency on a number of parameters, the results of these models have not been satisfactory. In this paper, we explore the use of Artificial Neural Networks for monsoon rainfall prediction. The techniques investigated include Backpropagation (BP) and Learning Vector Quantization (LVQ). We use 45 years real monsoon rainfall data from 1960 to 2004 for training of neural network models and evaluate the performance of these models over a test period of five years from 2005 to 2009. Comparison with Multiple Linear Regression and Statistical Downscaling Models reveals better performance of neural network techniques in terms of accuracy, and also in terms of greater lead time and fewer required resources.

I. Introduction

About 59 percent of annual rainfall in Pakistan comes from monsoon [1], making it the major source of rainfall. Monsoon in Pakistan starts from the month of July and lasts till the month of September. Due to its high spatiotemporal variability, monsoon rainfall prediction is a challenging task.