Predicting Sea Surface Temperatures in the North Indian Ocean with Nonlinear Autoregressive Neural Networks

Abstract

Prediction of monthly mean sea surface temperature (SST) values has many applications ranging from climate predictions to planning of coastal activities. Past studies have shown usefulness of neural networks (NNs) for this purpose and also pointed to a need to do more experimentation to improve accuracy and reliability of the results. The present work is directed along these lines. It shows usefulness of the nonlinear autoregressive type of neural network vis-à-vis the traditional feed forward back propagation type. Neural networks were developed to predict monthly SST values based on 61-year data at six different locations around India over 1 to 12 months in advance. The nonlinear autoregressive (NAR) neural network was found to yield satisfactory predictions over all time horizons and at all selected locations. The results of the present study were more attractive in terms of prediction accuracy than those of an earlier work in the same region. The annual neural networks generally performed better than the seasonal ones, probably due to their relatively high fitting flexibility.