
The significant wave height prediction holds critical value for marine energy development, coastal infrastructure planning, and ensuring safety in maritime operations. The precision of such predictions carries substantial theoretical and practical weight. This survey delivers an exhaustive evaluation and integration of the latest studies and advances in the domain of significant wave height prediction, serving as a methodical guidepost for academicians. The study introduces an all-encompassing predictive framework for significant wave height, which not only integrates diverse established forecasting techniques but also paves the way for novel research trajectories and creative breakthroughs. The framework is structured into four principal layers, i.e., feature selection, basic prediction, data decomposition, and parameter optimization. The ensuing sections meticulously dissect the methodologies within these strata, elucidating their core concepts, distinctive features, merits, and constraints, and their applicability to significant wave height prediction. To wrap up, the study delves into fresh research inquiries and avenues pertinent to the discipline, thereby broadening the comprehension of significant wave height prediction. In essence, this scholarly article imparts critical knowledge beneficial to the realm of marine technology.
data decomposition, TA168, feature selection, Electronic computers. Computer science, significant wave height prediction, parameter optimization, QA75.5-76.95, Systems engineering
data decomposition, TA168, feature selection, Electronic computers. Computer science, significant wave height prediction, parameter optimization, QA75.5-76.95, Systems engineering
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 5 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
