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首页> 外文期刊>Coastal engineering >Prediction and assimilation of surf-zone processes using a Bayesian network Part I: Forward models
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Prediction and assimilation of surf-zone processes using a Bayesian network Part I: Forward models

机译：使用贝叶斯网络的海浪区域过程的预测和同化第一部分：正向模型

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摘要

Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.
机译：可以从各种详细的地球物理过程模型中获得对沿海过程的预测，包括波浪，水流和沉积物的运移，其中许多模拟显示出显着的技能。此功能支持可以从准确的数值预测中受益的广泛研究和应用努力。但是，这些预测仅与用于驱动模型的数据一样准确，并且鉴于冲浪区的巨大时空变化，数据中的不准确性是不可避免的，因此有用的预测需要相应的不确定性估计。我们展示了如何在给定非常稀疏和/或不准确的边界条件数据的情况下，如何使用贝叶斯网络模型对海浪区域中的波高演变提供准确的预测。该方法基于对数据同化问题的形式化处理，该问题利用了显着降低模型系统维数的优势。我们证明了使用贝叶斯方法可以精确再现波浪演化的详细地球物理模型的预测。在此冲浪区应用中，前向预测技能为83％，模型输入中的不确定性准确地转换为输出变量中的不确定性。我们还证明，如果建模不确定性没有传达给贝叶斯网络（即假设采用了完美的数据或模型），则将计算出过于乐观的预测不确定性。通过将模型参数误差作为输入不确定性的来源，可以获得更一致的预测和不确定性。由于贝叶斯网络在估算波高的同时估算了最佳参数，因此实现了更好的预测（技能为90％）。

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  • 来源
    《Coastal engineering》 |2011年第1期|p.119-130|共12页
  • 作者

    Nathaniel G. Plant; K. Todd Holland;

  • 作者单位

    U.S. Geological Survey, 600 4th St. S., SI Petersburg, FL 33701, USA;

    Naval Research Laboratory, Stennis Space Center, MS 39529, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    wave height; bathymetry; field data; duck94;

    机译：波高测深仪现场数据;鸭94;

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