  In this paper, the complexities in the relationship between rainfall and sea
surface temperature (SST) anomalies during the winter monsoon
(November-January) over India were evaluated statistically using scatter plot
matrices and autocorrelation functions.Linear as well as polynomial trend
equations were obtained and it was observed that the coefficient of
determination for the linear trend was very low and it remained low even when
polynomial trend of degree six was used. An exponential regression equation and
an artificial neural network with extensive variable selection were generated
to forecast the average winter monsoon rainfall of a given year using the
rainfall amounts and the sea surface temperature anomalies in the winter
monsoon months of the previous year as predictors. The regression coefficients
for the multiple exponential regression equation were generated using
Levenberg-Marquardt algorithm. The artificial neural network was generated in
the form of a multiplayer perceptron with sigmoid non-linearity and
genetic-algorithm based variable selection. Both of the predictive models were
judged statistically using the Willmott index, percentage error of prediction,
and prediction yields. The statistical assessment revealed the potential of
artificial neural network over exponential regression.
