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

Published: 29 August 2018| Version 1 | DOI: 10.17632/htybmpsn6n.1
David Callaghan, Peter Mumby, Behnam Shabani, Tom Baldock


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.



Coastal Engineering