Incident wave run-up prediction using the response surface methodology and neural networks

Rehman, Khawar; Khan, Hassan; Cho, Yong-Sik; Hong, Seung Ho

doi:10.1007/s00477-021-02076-z

Cited by 7 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The non-parametric Kruskal–Wallis test is a technique often applied in statistical analyses. Mahdavi-Meymand et al 18 used the Kruskal–Wallis test to compare several machine learning (ML) techniques and empirical equations applied to predict spillways air demand and reported that there is no significant difference at the 99% confidence level between the applied ML approaches.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Modeling of wave run-up by applying integrated models of group method of data handling

Mahdavi‐Meymand

Zounemat‐Kermani

Sulisz

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Wave-induced inundation in coastal zones is a serious problem for residents. Accurate prediction of wave run-up height is a complex phenomenon in coastal engineering. In this study, several machine learning (ML) models are developed to simulate wave run-up height. The developed methods are based on optimization techniques employing the group method of data handling (GMDH). The invasive weed optimization (IWO), firefly algorithm (FA), teaching–learning-based optimization (TLBO), harmony search (HS), and differential evolution (DE) meta-heuristic optimization algorithms are embedded with the GMDH to yield better feasible optimization. Preliminary results indicate that the developed ML models are robust tools for modeling the wave run-up height. All ML models’ accuracies are higher than empirical relations. The obtained results show that employing heuristic methods enhances the accuracy of the standard GMDH model. As such, the FA, IWO, DE, TLBO, and HS improve the RMSE criterion of the standard GMDH by the rate of 47.5%, 44.7%, 24.1%, 41.1%, and 34.3%, respectively. The GMDH-FA and GMDH-IWO are recommended for applications in coastal engineering.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The results confirmed a good accuracy of the applied recurrent ANNs. Rehman et al 18 applied ANN and the response surface methodology (RSM) for wave run-up prediction. The obtained results showed that both the ANN and RSM are appropriate methods for the prediction of wave run-up.…”

Section: Introductionmentioning

confidence: 99%

Modeling of wave run-up by applying integrated models of group method of data handling

Mahdavi‐Meymand

Zounemat‐Kermani

Sulisz

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…This leads to computationally intensive and time-consuming simulations. In recent years, the CNN-based surrogate models have been trained on large datasets of wave and structure characteristics and corresponding wave run-up values [40]. The successfully trained model can then be used to quickly predict wave run-up for new wave conditions and structures.…”

Section: Machine Learning Capabilitiesmentioning

confidence: 99%