The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose.

使用基于物理学的波传播预测需要大量的时间投入，高水平的专业知识以及广泛的气候和礁石数据，而在进行海岸和珊瑚礁生态系统的管理规划时，这些数据并不总是可用。贝叶斯信念网络（BBN）至少具有三个属性，这使其成为传达基于物理学的波动模型预测的绝佳选择。首先，BBN包含成千上万的预测以提供概率结果。其次，通过使用先验概率，即使从业人员对输入参数的了解不完整，他们仍然可以获得波动结果的预测。第三，BBN可以将证据从输出传播到输入，这可以用来识别最有可能实现选定结果的输入条件。