Storm Surge Prediction for Louisiana Coast Using Artificial Neural Networks
Storm Surge Prediction for Louisiana Coast Using Artificial Neural Networks
复制标题
使用人工神经网络预测路易斯安那海岸风暴潮
DOI：
10.1007/978-3-319-46675-0_43
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
K. Hu
中科院分区：
文献类型：
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作者：
Qian Wang;Jianhua Chen;K. Hu
风暴潮是由飓风引起的近海水位上升，通常会导致洪水，对沿海地区的人民生命和财产造成严重破坏。为了减轻飓风的影响，必须及时准确地预测风暴潮水平。传统的基于过程的风暴潮预测数值模型受到高计算需求的限制，使得及时预测变得困难。在这项工作中，开发了基于人工神经网络 (ANN) 的系统来预测路易斯安那州沿海地区的风暴潮。收集模拟和历史风暴数据分别用于模型训练和测试。使用美国国家海洋和大气管理局 (NOAA) 提供的历史飓风参数和飓风事件期间潮汐站的潮汐数据进行实验。结果分析表明，我们基于人工神经网络的风暴潮预测器可以有效地产生准确的预测。
Storm surge, an offshore rise of water level caused by hurricanes, often results in flooding which is a severe devastation to human lives and properties in coastal regions. It is imperative to make timely and accurate prediction of storm surge levels in order to mitigate the impacts of hurricanes. Traditional process-based numerical models for storm surge prediction suffer from the limitation of high computational demands making timely forecast difficult. In this work, an Artificial Neural Network (ANN) based system is developed to predict storm surge in coastal areas of Louisiana. Simulated and historical storm data are collected for model training and testing, respectively. Experiments are performed using historical hurricane parameters and surge data at tidal stations during hurricane events from the National Oceanic and Atmospheric Administration (NOAA). Analysis of the results show that our ANN-based storm surge predictor produces accurate predictions efficiently.