PDF(Pubmed)
Abstract：
Huge waves caused by typhoons often induce severe disasters along coastal areas, making the effective prediction of typhoon-induced waves a crucial research issue for researchers. In recent years, the development of the Internet of Underwater Things (IoUT) has rapidly increased the prediction of oceanic environmental disasters. Past studies have utilized meteorological data and feedforward neural networks (e.g., BPNN) with static network structures to establish short lead time (e.g., 1 h) typhoon wave prediction models for the coast of Taiwan. However, sufficient lead time for prediction remains essential for preparedness, early warning, and response to minimize the loss of lives and properties during typhoons. The aim of this research is to construct a novel long lead time typhoon-induced wave prediction model using Long Short-Term Memory (LSTM), which incorporates a dynamic network structure. LSTM can capture long-term information through its recurrent structure and selectively retain necessary signals using memory gates. Compared to earlier studies, this method extends the prediction lead time and significantly improves the learning and generalization capability, thereby enhancing prediction accuracy markedly.
摘要：
台风引起的巨浪经常在沿海地区引发严重的灾害，使台风诱发波的有效预测成为研究人员的关键问题。近年来,水下物联网（IoUT）的发展迅速增加了对海洋环境灾害的预测。过去的研究利用了气象数据和前馈神经网络(例如，BPNN)具有静态网络结构，以建立较短的提前期(例如，1h)台湾沿海的台风波浪预报模型。然而,足够的预测提前期对于做好准备仍然至关重要，预警,和响应，以最大程度地减少台风期间的生命和财产损失。这项研究的目的是建立一个新的长提前期台风诱发波预测模型，使用长短期记忆（LSTM），它包含了一个动态的网络结构。LSTM可以通过其循环结构捕获长期信息，并使用存储门选择性地保留必要的信号。与早期的研究相比，该方法延长了预测提前期，显著提高了学习和泛化能力，从而显著提高预测精度。
