Wave Prediction and Data Supplementation with Artificial Neural Networks
Wave Prediction and Data Supplementation with Artificial Neural Networks
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DOI：
10.2112/04-0407.1
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发表时间：
2007-07
期刊：
影响因子：
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通讯作者：
Oleg Makarynskyy;D. Makarynska
中科院分区：
文献类型：
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作者：
Oleg Makarynskyy;D. Makarynska
海岸带基础设施的成功开发和管理在很大程度上取决于对当前和未来海浪条件的正确估计。本文提出了一种特定于现场的人工神经网络方法，作为在变化的海岸环境中进行波浪参数现在和预报的基本工具。该工具可以替代需要确定性海浪模型的数据和计算工作。使用的数据是在塔斯马尼亚海岸附近的一次实地考察中收集的。开展这项运动是为了估计塔斯马尼亚地区的海况和海浪条件的时间变异性。这项研究被认为是这场运动的合乎逻辑的延伸。这些数据首次用于神经网络研究。所审议的期间因地点不同而有所不同，由1985年9月至1993年12月。根据偏差、均方根误差、相关系数和散布指数对神经模拟的质量与独立测量进行评估。三种不同架构的神经网络以类似的方式运行，并提前3小时和6小时产生可靠的预测。警告时间越长，准确率就越低。同时，波参数的跟踪和反演表现出稳定、高质量的特点。结果表明，该方法在不同地形条件下进行波浪预报和资料补充是可行的。
Abstract Successful development and management of coastal zone infrastructure is to a great extent based on proper estimates of current and future wave conditions. In this paper, a site-specific artificial neural network methodology is proposed to serve as a basic tool for both now and forecasting of wave parameters in variable coastal environments. This tool is an alternative to data and computational effort demanding deterministic wave models. The data used were collected during a field campaign near the Tasmanian coast. The campaign was undertaken to estimate sea states and temporal variability of wave conditions in the Tasmanian region. This study is considered to be a logical extension of that campaign. These data were used in a neural network study for the first time. The periods under consideration differ for different sites and extend from September 1985 to December 1993. The quality of the neural simulations was assessed vs. the independent measurements in terms of the bias, root mean square error, correlation coefficient, and scatter index. Neural networks of three different architectures perform in a similar way and produce reliable predictions 3 and 6 hours ahead. The accuracy drops for larger warning times. Meanwhile, the tracking and retrieval of wave parameters exhibit stable, high quality. Thus, the results show the feasibility of the methodology for wave forecasting and data supplementation in essentially different topographic conditions.