Predicting cyclone tracks in the north Indian Ocean: An artificial neural network approach

Details Predicting cyclone tracks in the north Indian Ocean: An artificial neural network approach Predicting cyclone tracks in the north Indian Ocean: An artificial neural network approach

Feb 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007georl..3404603a&link_type=abstract

Geophysical Research Letters, Volume 34, Issue 4, CiteID L04603

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Global Change: Oceans (1616, 3305, 4215, 4513), Oceanography: General: Ocean Predictability And Prediction (3238), Mathematical Geophysics: Prediction (3245, 4263), Computational Geophysics: Neural Networks, Fuzzy Logic, Machine Learning

Scientific paper

Predicting cyclone tracks in the Indian Ocean has been a challenging problem. In this paper, we used past 12 hours of observations (2 positions, at 6 hourly intervals and the present position) to predict the position of a cyclone 24 hours in advance in terms of latitude and longitude. For this purpose we adopted an artificial neural network approach using 32 years (1971-2002) of tropical cyclone best track analysis over the Indian Ocean. The mean absolute error between the estimated and actual latitude (longitude) is 0.75 (0.87) degrees with correlation coefficient of 0.98 (0.99) for the prediction data set that was not used for developing the model. The mean error of estimation of the distance between the best track and the predicted positions is 137.5 km. Forecasts for 12, 36, 48, 60 and 72 hours were also attempted.

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