小波
灵敏度(控制系统)
模糊逻辑
有效波高
均方误差
小波变换
奇异谱分析
奇异值分解
预测建模
计算机科学
波高
数学
算法
数据挖掘
统计
人工智能
风浪
工程类
地质学
海洋学
电子工程
作者
Abdüsselam Altunkaynak,Anıl Çelik,Murat Barış Mandev
标识
DOI:10.1016/j.oceaneng.2023.114771
摘要
Generation of significant wave height (SWH) is considered as a complex and stochastic dynamical process whose prediction is vital for effective marine, ocean engineering, sustainable development and scientific phenomena. Based on literature review, despite numerous research attempts to forecast significant wave height with short prediction time horizons, they are incapable in yielding accurate SWH predictions. To achieve this end wavelet techniques have been intensively employed as data pre-processing tools and, have been incorporated with soft computing based approaches to improve the prediction performance of developed models. However, wavelet algorithm has some limitations such as shift sensitivity, poor directionality and lack of phase information. In addition, this technique suffers from time consuming complicated mathematical procedures. In the present study, as a way of addressing the shortcomings of wavelet tool and enhancing prediction accuracy with extended time horizons, singular spectrum analysis (SSA) is proposed as a decomposition procedure. The prediction accuracy of the three distinct models is contrasted by means of diagnostic metrics, Mean Square Error (MSE), the Nash-Sutcliffe Coefficient of efficiency (CE) and determination of coefficient (R2). With its lowest MSE and highest CE value SSA-Fuzzy model clearly outperformed the stand alone Fuzzy and W-Fuzzy models in predicting hourly SWH for all stations and future time horizons. This implies that SSA technique has the utmost capability to decompose measured data effectively into its deterministic and stochastic components.
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