Prediction of solitary wave attenuation by emergent vegetation using genetic programming and artificial neural networks

Analyzing the attenuation of extreme waves by coastal emergent vegetation provides crucial information on revetment planning. In this study, three kinds of laboratory experiments of wave attenuation by rigid vegetation are performed. Transmission coefficient (K_t) was used to characterize the effect of wave attenuation. The influence of dimensionless factors including relative wave height (H/h), relative width (B/L), relative height (h_v/h) and solid volume fraction (ϕ) on the K_t under the action of solitary wave was explored by Genetic Programming (GP), Artificial Neural Networks (ANNs) and multivariate non-linear regression (MNLR). Prediction formulae (R² is up to 0.95) of the K_t in different models were established by GP method, and the sensitivity of each dimensionless factor was analyzed by statistical analysis. ANNs were used to compare the weight of each factor. The power function relationships between K_t and factors was obtained by MNLR. The results show that GP can qualitatively acquire the sensitivity of parameters and is suitable for the sensitivity analysis of the vegetation wave disspation model, providing a more efficient and accurate prediction method. The results can provide guidelines for vegetation planting as well as the scientific basis for vegetation revetment engineering.