## 1. Introduction

[2] Several models and methods have been developed to predict the positions of the cyclones accurately to issue appropriate warning for disaster management. *Mohanty and Gupta* [1997] and *Gupta* [2006] described different track prediction techniques. *Bell* [1979] described different operational numerical forecasting models. Storm surge is the most devastating impact of the tropical cyclones, particularly, for the Indian coastal regions. Because of the highly varying bathymetry of the Indian region, even a slight error in the prediction of landfall point can lead to a totally different storm surge height. Hence, it is of importance to predict the position of the cyclone in advance with sufficient accuracy. Compared with north Atlantic region, errors in the track prediction have not significantly reduced over the north Indian Ocean [*Gupta*, 2006]. The objective track prediction of tropical cyclones may be grouped into four categories [*Elsberry*, 1995]: (1) empirical, e.g., climatology, persistence of past motion, climatology and persistence (CLIPER), and analogue techniques; (2) statistical-synoptic, in which additional meteorological information is incorporated, usually via statistical regression using grid-point values from synoptic analysis available at the forecast time; (3) statistical-dynamic, in which grid-point values from synoptic predictions are also incorporated; and (4) dynamic, in which a global or regional numerical weather prediction (NWP) model is integrated as an initial value problem to provide a track forecast. The empirical track forecasts are easy to understand given the simple inputs. On the other hand, the statistical models have additional complexity and are not easy to interpret because the grid-point predictions are generally not available to the forecaster. The dynamical models are even more complex and difficult in which steering influences at many levels and various physical processes such as advective, adiabatic and frictional effects may be contributing to cyclone motion.

[3] Traditionally, modeling a dynamical system requires deriving the equations of motion from first principles, to measure initial conditions and, finally, to integrate the equations of motion forward in time. Alternatively, when such an approach is not feasible due to some reasons, empirical laws governing the physical processes can be obtained by model-fitting approaches based on the observed variability of the system evolution. *Lam* [1993] used the NWP model for 24 hours advance prediction of cyclone track. But the most sophisticated models in operation could not escape large forecast errors. According to *Carr and Elsberry* [2000], about one third of the 72-hours forecasts in 1997 had errors exceeding 555 km. *Heming et al.* [1995] pointed peculiarities of the tropical cyclone problem in the NWP context.

[4] In this paper, we used the artificial neural network (ANN) approach to predict the position of the Indian Ocean cyclones 24 hours in advance using only the past 12-hour locations at six hourly intervals besides the present position. Neural networks have been applied to a wide variety of areas like physical oceanography, biological oceanography, meteorology, acoustics, robotics and medical sciences [*Nannariello and Fricke*, 1998; *Chen et al.*, 2000; *Richaume et al.*, 2000; *Pozzi et al.*, 2000; *Schroeder et al.*, 2002; *Bourras and Liu*, 2003; *Ali et al.*, 2004; *Jain and Ali*, 2006]. ANN model has been used to predict the hurricane intensity in the north Atlantic basin [*Ramirez and Castro*, 2006; *Ramirez and Veneros*, 2004]. *Baik and Paek* [2000] compared linear regression method and ANN approach in predicting the cyclone intensity and found that ANN scheme has improved the prediction. For the development and prediction of the ANN model, we used 32 years (1971–2002) of best track analysis from Joint Typhoon Warning Center (JTWC), USA that provides cyclone positions at 6 hourly intervals.