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

机译：使用贝叶斯网络对海浪区域过程进行预测和同化第二部分：逆模型

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A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part 1). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R~2 = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters.
机译：已经开发出贝叶斯网络模型来模拟在冲浪区中波传播的相对简单的问题（在第1部分中详细介绍）。在这里，我们证明了这种贝叶斯模型可以提供逆建模和数据同化解决方案，以预测海上波浪高度和深度估计值，前提是从陆上位置获得有限的波浪高度和深度信息。扩展了逆方法，以允许使用与问题的确定性解决方案不兼容的观测输入进行数据同化。这些输入包括沙洲位置（而不是测深法）和波浪破碎强度的估计值（而不是波浪高度观测值）。我们的结果表明，破波信息对于减少预测误差至关重要。在许多实际情况下，可以从岸上观察者或遥感系统提供此信息。我们表明，同化输入的各种组合显着减少了模型域中水深和波高估计的不确定性。贝叶斯网络模型在新的现场数据中的应用证明了对近一个月的离岸海浪高度时间序列反演的显着预测技巧（R〜2 = 0.7）。贝叶斯逆结果包括不确定性估计，在给定输入不确定性（例如深度和调整参数）时，不确定性估计最准确。此外，逆建模被扩展为直接估计与基础波过程模型相关的调谐参数。模型参数的逆估计不仅显示出与先前研究结果一致的海上波浪高度依赖性，而且调整参数的不确定性估计也解释了先前报道的模型参数的变化。

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