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Machine learning based prediction of wave breaking over a fringing reef

机译：基于机器学习的边缘珊瑚礁破波预测

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The characteristics of wave breaking in shallow waters that are of interest include whether a wave will break, the type of breaking that will occur, the breaking wave height, breaking depth, the position of breaking, the wave setup, and the transformation of the broken wave for given offshore wave characteristics and given bottom profile. Various methods have been proposed in the literature to estimate these wave-breaking characteristics. Deo et al. (2003) used a neural network approach to predict the breaking wave height and breaking depth for waves transforming over a range of simply sloped bottoms. The Deo et al. approach is extended here to predict other characteristics of wave breaking, including the type of wave breaking, the position of breaking, the wave setup, and the rate of dissipation of wave energy, in the case of waves impinging on a fringing reef. Observations from a series of specially conducted laboratory experiments involving monochromatic waves impinging on an idealized reef are used to develop and train respective models. The input parameters to the neural network models are the ratio of offshore wave height to the shallow-water depth of the flat section of the reef, H-1/h(s) and the wave frequency parameter f root H-1/g. The breaker type classification model developed predicts the type of breaker with a success rate of 96%, outperforming previously used criteria for classifying breaker types. The numeric prediction models for the dimensionless position of wave breaking for plunging and spilling breakers, for wave setup, and for the reduction in energy flux across the reef have performance ratings characterized by respective correlation coefficients of 0.99, 0.82, 0.89, and 0.94. The modest value for the correlation between prediction and the actual result for the position of breaking of spilling breakers is believed to be associated with inaccuracies in determination of the exact position of breaking and to difficulty in visually capturing spilling breakers in observations. High correlation between predicted and actual values of the reduction in energy flux across the reef is achieved in spite of the fact the model was trained using data from a wave tank that included partial reflection (characterized by 7% mean deviation among non-breaking waves) from the downstream end of the tank. The method can be extended to provide predictive models for consideration of a range of natural coastal conditions, random waves, and various bottom profiles and complex geometry, based on training and testing of the models using representative laboratory, field, and/or flow simulation, in support of accurate prediction of near-shore wave phenomena.
机译：感兴趣的浅水波破裂特征包括波浪是否会破裂，将发生的破裂类型，破裂波的高度，破裂深度，破裂的位置，波浪的建立以及破裂的转变给定海上波浪特征和给定底部轮廓的波浪。文献中已经提出了各种方法来估计这些波浪破碎特性。 Deo等。 （2003年）使用神经网络方法来预测在简单的倾斜底部范围内转换的波浪的破碎波高度和破碎深度。 Deo等。在这里扩展方法来预测波浪破碎的其他特征，包括波浪类型，破碎的位置，波浪的形成以及波浪撞击边缘珊瑚礁时波浪能量的耗散率。来自一系列专门进行的实验室实验的观察结果被用于开发和训练各个模型，这些实验涉及单色波撞击在理想化的礁石上。神经网络模型的输入参数是海洋波浪高度与礁石平坦部分浅水深度之比H-1 / h（s）和波浪频率参数f root H-1 / g。开发的断路器类型分类模型可以预测断路器的类型，其成功率为96％，胜过先前用于对断路器类型进行分类的标准。对于跌落式和溢出式破碎器的破波无量纲位置，波形成以及礁石上能量通量减少的数值预测模型，其性能额定值的特征是相关系数分别为0.99、0.82、0.89和0.94。据信，对于泄漏断路器的破损位置的预测与实际结果之间的相关性的适度值与确定断裂的确切位置的不精确性有关，并且与在观察中视觉上捕获泄漏断路器的难度有关。尽管通过使用包含部分反射的波浪舱数据对模型进行了训练，但仍实现了跨礁能量通量减少的预测值与实际值之间的高度相关性（以不间断波浪之间的7％平均偏差为特征）从水箱的下游端开始。该方法可以扩展为提供预测模型，以考虑使用代表性实验室，田野和/或流量模拟对模型进行训练和测试，从而考虑到一系列自然沿海条件，随机波浪以及各种底部剖面和复杂的几何形状，支持准确预测近岸波浪现象。

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