Maximum Sustained Wind Speed Simulation of Storm Surge with Long Short-Term Memory

被引：0
|
作者：
Tun, A. Me [1 ]
Khine, May Aye [1 ]
机构：
[1] Univ Comp Studies, Yangon, Myanmar
来源：
2019 4TH IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - ASIA (IEEE ICCE-ASIA 2019) | 2019年
关键词：
Long Short-Term Memory (LSTM); Artificial Neural Network (ANN); Maximum Sustained Wind Speed;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Tropical cyclones threatened many countries around the Bay of Bengal as storm surges. India, Bangladesh, and Myanmar have much destruction along the coastal regions due to storm surge. So, storm surge prediction needs to be accurate. Traditional process-based numerical models have high computational demands to make timely forecast and deterministic numerical models are strongly dependent on accurate meteorological input to predict storm surge. In this work, a Long Short-Term Memory Neural Network (LSTM) used to simulate the maximum sustained wind speed of storm in coastal areas of the Bay of Bengal and the Arabian Sea. Simulated and historical storm data are collected from the Regional Specialized Meteorological Centre (RSMC).
引用
收藏
页码：18 / 19
页数：2
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