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Item
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Maximum Sustained Wind Prediction of Storm Surge in Bay of Bengal
http://hdl.handle.net/20.500.12678/0000004796
http://hdl.handle.net/20.500.12678/00000047960e404e15-cc66-4598-a4a9-f67c16aa16ee
005c0dc0-1672-4b08-89c8-74fb5978bef6
Name / File | License | Actions |
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Article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Maximum Sustained Wind Prediction of Storm Surge in Bay of Bengal | |||||
Language | en | |||||
Publication date | 2017-02-16 | |||||
Authors | ||||||
Tun, A Me | ||||||
Khine, May Aye | ||||||
Description | ||||||
Most of the countries around the Bay ofBengal are threatened by storm surges associatedwith severe tropical cyclones. The destruction alongthe coastal regions of India, Bangladesh, andMyanmar are serious due to the storm surge. Tomitigate the impacts of tropical storm, the predictionof storm surge need to be accurate. Traditionalprocess-based numerical models have the limitationof high computational demands to make timelyforecast and deterministic numerical models arestrongly dependent on accurate meteorological inputto predict storm surge. In this work, a Multilayerperceptron (MLP) and a Radial Basic FunctionNetwork (RBFN) used to predict the maximumsustained wind speed in knots (VMAX) of storm incoastal areas of Bay of Bengal. The ANN networkmodel provides fast, real-time storm surge estimatesat Bay of Bengal. Simulated and historical storm dataare collected for model training and testing,respectively. North India Ocean Best Track Datafrom Joint Typhoon Warning Center (JTWC) used toperform experiments. The result of MLP is predictedVMAX value closer than in RBFN prediction. | ||||||
Keywords | ||||||
Artificial Neural Network, storm surge, JTWC | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/691 | |||||
Journal articles | ||||||
Fifteenth International Conference on Computer Applications(ICCA 2017) | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |