Data for: Communicating physics-based wave model predictions of coral reefs using Bayesian Belief Networks

Bayesian belief network files for beach toe significant wave conditions on coral reefs, developed using wave predictions from Baldock et al (2015).

There is one network (Hs_toe_*.neta, Netica v5.18 files) that has been trained using the case file Hs_toe.cas, with three different learning algorithms, counting (Hs_toe_C.neta), expectation-maximization (Hs_toe_EM.neta) and gradient descent (Hs_toe_GA.neta).

Reference
Baldock, T.E., Golshani, A., Atkinson, A., Shimamoto, T., Wu, S., Callaghan, D.P. and Mumby, P.J., 2015. Impact of sea-level rise on cross-shore sediment transport on fetch-limited barrier reef island beaches under modal and cyclonic conditions. Marine Pollution Bulletin.

基于Baldock等（2015年）波浪预测的珊瑚礁海滩显著波浪条件贝叶斯信念网络文件，该文件采用Netica v5.18软件，利用Hs_toe.cas案例文件，通过三种不同的学习算法进行训练，包括计数（Hs_toe_C.neta）、期望最大化（Hs_toe_EM.neta）和梯度下降（Hs_toe_GA.neta）。参考文献：Baldock, T.E.，Golshani, A.，Atkinson, A.，Shimamoto, T.，Wu, S.，Callaghan, D.P. 和 Mumby, P.J.，2015. 海平面上升对潮汐限定的屏障岛海滩的横岸泥沙运输影响研究——在常态和气旋条件下。海洋污染通报。