The present study aimed to predict the maximum seasonal wave height by new integrative data driven methods. For this purpose, two data-driven techniques, that are, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector Regression (SVR), were applied, and a BWO algorithm was used as an integrated method (ANFIS-BWO and SVR-BWO). In addition, the Particle Swarm Optimization (PSO) algorithm was used as a method integrated with SVR and ANFIS (SVR-PSO and ANFIS-PSO) to compare the performance of the newly developed methods (ANFIS-BWO and SVR-BWO). The wave data were collected in different seasons by a buoy station deployed in the southern Baltic Sea by the Institute of Hydro-Engineering of the Polish Academy of Sciences. Seasonal simulations were performed to investigate the effect of seasons on the maximum wave height. The wave data constituted an unevenly spaced time series. The maximum wave height was modeled using the maximum wave height period (T_max), the significant wave height (H_s), the significant wave period (T_s), and time steps (Δt). The results showed that the application of BWO and PSO algorithms increased the accuracy of ANFIS and SVR by about 18.45%. Moreover, the results show that PSO increased the accuracy of ANFIS and SVR by about 17.98% and 21.59%, respectively. The results of different runs indicated that the BWO is more stable to reach the global solution than PSO. The results also show that show that SVR-BWO is the most accurate model.

本研究旨在通过新的综合数据驱动方法来预测最大季节性波高。为此，应用了两种数据驱动技术，即自适应神经模糊推理系统（ANFIS）和支持向量回归（SVR），并将BWO算法用作集成方法（ANFIS-BWO和SVR-BWO）。此外，使用粒子群优化（PSO）算法作为与SVR和ANFIS（SVR-PSO和ANFIS-PSO）集成的方法，以比较新开发的方法（ANFIS-BWO和SVR-BWO）的性能。波兰科学院水利工程研究所在波罗的海南部部署的浮标站收集了不同季节的海浪数据。进行季节模拟以调查季节对最大波浪高度的影响。波浪数据构成了不均匀间隔的时间序列。最大波高是使用最大波高周期（T _max），有效波高（H _s），有效波周期（T _s）和时间步长（Δt）。结果表明，BWO和PSO算法的应用使ANFIS和SVR的准确性提高了约18.45％。此外，结果表明，PSO分别将ANFIS和SVR的准确性提高了约17.98％和21.59％。不同运行的结果表明，BWO比PSO更稳定地达到全局解决方案。结果还表明，SVR-BWO是最准确的模型。