Multivariate forecast of winter monsoon rainfall in India using SST anomaly as a predictor: Neurocomputing and statistical approaches

TL;DR

This study compares statistical and neural network models for predicting winter monsoon rainfall in India using SST anomalies, finding neural networks outperform exponential regression in forecast accuracy.

Contribution

It introduces an artificial neural network approach with variable selection for monsoon rainfall prediction, demonstrating its superiority over traditional exponential regression models.

Findings

Neural network models showed higher prediction accuracy than exponential regression.

Statistical evaluation confirmed the neural network's better performance.

Linear and polynomial trends poorly captured rainfall variability.

Abstract

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.