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 | | Neural networks (Computer science), Cyclone forecasting | Computer programs
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|  | Neural network methodologies for cyclone wind intensity and path prediction
Author: Deo, Ratneel Vikash
Institution: The University of the South Pacific
Award: Master of Science
Subject: Neural networks (Computer science), Cyclone forecasting | Computer programs
Date: 2017
Call No.: Pac QA 76 .87 .D46 2017
BRN: 1361114
Copyright:Under 10% of this thesis may be copied without the authors written permission
Abstract: Tropical cyclone wind-intensity and path prediction are challenging tasks considering drastic changes of the climate patterns over the last few decades. Cyclones cause extensive damage to everything in its path, however, the destruction caused by this natural calamity could be reduced immensely with accurate and timely forecasts of cyclone track and intensity. The unpredictable nature of cyclones are dicult for statistical predicting models to learn and make ecient and timely predictions. Cyclones have been studied extensively and statistical models have been used to make predictions. Time series prediction relies on past data points to make robust predictions. Recurrent neural networks have been suitable for time series prediction due to their architectural properties in modeling temporal sequences.
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