Institute of Hydro-Engineering, Polish Academy of Sciences, Warsaw, Poland.
Water Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.
Sci Rep. 2022 May 18;12(1):8279. doi: 10.1038/s41598-022-12038-2.
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.
沿海地区的波浪侵袭是居民面临的一个严重问题。准确预测波浪爬高是海岸工程中的一个复杂现象。在本研究中，开发了几种机器学习 (ML) 模型来模拟波浪爬高。所开发的方法基于使用数据处理组方法 (GMDH) 的优化技术。入侵杂草优化 (IWO)、萤火虫算法 (FA)、基于教与学的优化 (TLBO)、和声搜索 (HS) 和差分进化 (DE) 元启发式优化算法被嵌入 GMDH 中以产生更好的可行优化。初步结果表明，所开发的 ML 模型是建模波浪爬高的强大工具。所有 ML 模型的准确性都高于经验关系。结果表明，采用启发式方法可以提高标准 GMDH 模型的准确性。因此，FA、IWO、DE、TLBO 和 HS 分别将标准 GMDH 的 RMSE 准则提高了 47.5%、44.7%、24.1%、41.1%和 34.3%。建议在海岸工程中应用 GMDH-FA 和 GMDH-IWO。
