%0 Journal Article
%T Predicting Sea Surface Temperatures in the North Indian Ocean with Nonlinear Autoregressive Neural Networks
%A Kalpesh Patil
%A M. C. Deo
%A Subimal Ghosh
%A M. Ravichandran
%J International Journal of Oceanography
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/302479
%X 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-¨¤-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. 1. Introduction The temperature of water at around 1£żm below the ocean surface, commonly referred to as sea surface temperature (SST), is an important parameter to understand the exchange of momentum, heat, gases, and moisture across air-sea interface. Its knowledge is necessary to explain and predict important climate and weather processes including the summer monsoon and El-Nino events. SST predictions are sought after by the users of coastal communities dealing with fishing and sports. Like the air above it SST changes significantly over time, although relatively less frequently due to a high specific heat. The changes in water temperature over a vertical are high at the sea surface due to large variations in the heat flux, radiation, and diurnal wind near the surface, and hence SST estimations involve considerable amount of uncertainty. There are a variety of techniques for measuring SST. These include the thermometers and thermistors mounted on drifting or moored buoys and remote sensing by satellites. In case of satellites the ocean radiation in certain wavelengths of an electromagnetic spectrum is sensed and related to SST. Microwave radiometry based on an imaging radiometer called the moderate resolution imaging spectroradiometer is also popularly used to record SST. In order to predict SST physicallybased as well as data driven methods are practiced. The latter type is many times preferred when site specific information is required and considering the convenience. The
%U http://www.hindawi.com/journals/ijocean/2013/302479/