This study investigates the ability of a new hybrid neuro-fuzzy model by combining the neuro-fuzzy (ANFIS) approach with the marine predators’ algorithm (MPA) in predicting short-term (from 1 h ahead to 1 day ahead) significant wave heights. Data from two stations, Cairns and Palm Beach buoy, were used in assessing the considered methods. The ANFIS-MPA was compared with two other hybrid methods, ANFIS with genetic algorithm (ANFIS-GA) and ANFIS with particle swarm optimization (ANFIS-PSO), in predicting significant wave height for multiple lead times ranging from 1 h to 1 day. The multivariate adaptive regression spline was investigated in deciding the best input for prediction models. The ANFIS-MPA model generally offered better accuracy than the other hybrid models in predicting significant wave height in both stations. It improved the accuracy of ANFIS-PSO and ANFIS-GA by 8.3% and 11.2% in root mean square errors in predicting a 1 h lead time in the test period.

The study examines the applicability of six metaheuristic regression techniques—M5 model tree (M5RT), multivariate adaptive regression spline (MARS), principal component regression (PCR), random forest (RF), partial least square regression (PLSR) and Gaussian process regression (GPR)—for predicting short-term significant wave heights from one hour to one day ahead. Hourly data from two stations, Townsville and Brisbane Buoys, Queensland, Australia, and historical values were used as model inputs for the predictions. The methods were assessed based on root mean square error, mean absolute error, determination coefficient and new graphical inspection methods (e.g., Taylor and violin charts). On the basis of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) statistics, it was observed that GPR provided the best accuracy in predicting short-term single-time-step and multi-time-step significant wave heights. On the basis of mean RMSE, GPR improved the accuracy of M5RT, MARS, PCR, RF and PLSR by 16.63, 8.03, 10.34, 3.25 and 7.78% (first station) and by 14.04, 8.35, 13.34, 3.87 and 8.30% (second station) for the test stage.

Constructing underground reservoirs has emerged as a crucial strategy to address the shortage of fresh water in Rizhao, Shandong Province, China. However, the water quality, microbial community composition, and biogeochemical cycling of nutrients in underground reservoirs compared to raw water remain unknown. To unveil the characteristics of microbial community structures and their nitrogen cycling metabolic potential in coastal underground reservoirs, we utilized a functional gene array (GeoChip 5.0) in conjunction with high-throughput sequencing of 16S rRNA and 18S rRNA genes. Our findings indicate that the water quality in the underground reservoir exhibits a certain degree of eutrophication compared to raw water, with higher concentrations of TN, TP, NO₃⁻N, NO₂⁻-N, and Chl a, but lower concentrations of DO and NH₄⁺-N. The alpha diversity of bacterial and microeukaryotic communities was significantly lower in the underground reservoir. The bacterial community presented a stronger correlation with environmental factors than the microeukaryotic community. Regarding the relative abundance of bacterial communities, Gammaproteobacteria dominated the bacterial community in raw water, while Gammaproteobacteria and Alphaproteobacteria dominated the bacterial community in underground reservoir water. Additionally, the relative abundance of Nitrospirae was noticeably higher in the underground reservoir water. Moreover, we found significantly higher sequence abundance of the archaea Thaumarchaeota in the underground reservoir. Furthermore, our analysis revealed that, except for the amoA functional gene, which significantly increased the metabolic potential of nitrification, the metabolic potential of other microbial nitrogen functional genes was significantly reduced. This reduction may contribute to the lower concentration of NH₄⁺-N in the underground reservoir. This study provides a comprehensive understanding of the microbial community characteristics and their nitrogen cycling metabolic potential in underground reservoirs. It serves as a valuable reference for water source selection, the formulation of water quality assurance measures, and the construction and management of underground reservoirs for subsequent impounding.