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

A. M. Tun, May Aye Khine
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引用次数: 0

Abstract

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).
具有长短期记忆的风暴潮最大持续风速模拟
热带气旋以风暴潮的形式威胁着孟加拉湾周围的许多国家。由于风暴潮,印度、孟加拉国和缅甸沿海地区遭受了严重破坏。因此,风暴潮预测需要准确。传统的基于过程的数值模式对计算量的要求较高,难以及时预报,而确定性数值模式在预测风暴潮时强烈依赖于准确的气象输入。本文利用长短期记忆神经网络(LSTM)模拟了孟加拉湾和阿拉伯海沿岸地区风暴的最大持续风速。模拟及历史风暴资料由区域专业气象中心(RSMC)收集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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