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

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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.

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