基于人工神经网络的台风波浪过顶预测模型的建立

Seung-woo Kim, H. Lee, Hyukjin Choi
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引用次数: 0

摘要

本文利用该数据库,通过波浪水槽数值模拟,建立了台风波浪超顶的人工神经网络(ANN)模型。所建立的人工神经网络模型大大节省了计算时间。模型的精度也接近数值模拟的95%以上。通过与数值模拟目标数据和人工神经网络模型输出结果的相关系数和均方根误差来评价其精度。该模型在不需要高保真数值模型和高计算资源的情况下,可以快速得出海岸道路中点的平均浪过顶率、最大浪高、最大浪过顶深度和速度。这意味着包括人工神经网络模型在内的台风预警系统比目前使用的仅仅是监测预警系统更强大和有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of a typhoon wave overtopping prediction model based on an artificial neural network
In this study, an artificial neural network (ANN) model for the typhoon wave overtopping was developed using the database by a numerical wave flume simulation. The developed ANN model is effective for saving calculation time largely. The accuracy of the model is also approached to over 95% of the numerical simulation. This accuracy was evaluated by the correlation coefficient and the root mean square error with the target data of the numerical simulation and output of the ANN model. This model quickly produces the mean wave overtopping rate, maximum wave run-up height, maximum wave overtopping depth and velocity at the middle point in the coastal road without high-fidelity numerical model and high-computing resources. It means that the typhoon warning system including the ANN models is powerful and useful rather than only the monitoring warning system currently in use.
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