Abstract :

Hurricanes, Which Are Powerful Tropical Cyclones Originating In The Atlantic Basin, Pose Significant Threats To Both Life And Infrastructure Upon Landfall. Anticipating Their Trajectory Is Essential To Mitigate Associated Risks. Traditional Predictive Techniques Often Fall Short Due To The Inherent Complexity And Variability Of Storm Paths. In This Research, We Employ Long Short-Term Memory (LSTM) Networks, A Class Of Deep Learning Models Known For Handling Sequential Data, To Forecast Hurricane Trajectories. Given Their Ability To Retain Long-term Dependencies, LSTMs Are Well-suited For Sequence Modeling Tasks Such As This. The Proposed Model Is Trained On Relevant Meteorological Parameters To Learn The Patterns Influencing Storm Movements And Deliver More Accurate Path Predictions. Keywords— Hurricane, Deep Learning, LSTM, RNN.

Published:

16-12-2022

Issue:

Vol. 22 No. 12 (2022)

Page Nos:

26-30

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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