Significant wave height forecasting using wavelet fuzzy logic approach

Wave heights and periods are the significant inputs for coastal and ocean engineering applications. These applications may require to obtain information about the sea conditions in advance. This study aims to propose a forecasting scheme that enables to make forecasts up to 48 h lead time. The combination of wavelet and fuzzy logic approaches was employed as a forecasting methodology. Wavelet technique was used to separate time series into its spectral bands. Subsequently, these spectral bands were estimated individually by fuzzy logic approach. This combination of techniques is called wavelet fuzzy logic (WFL) approach. In addition to WFL method, fuzzy logic (FL), artificial neural networks (ANN), and autoregressive moving average (ARMA) methods were employed to the same data set for comparison purposes. It is seen that WFL outperforms those methods in all cases. The superiority of the WFL in model performances becomes very clear especially in higher lead times such as 48 h. Significant wave height and average wave period series obtained from buoys located off west coast of US were used to train and test the proposed models.     

Estimation of extreme wave heights using GEOSAT measurements

Satellite technology has yielded a large database of global ocean wave heights which may be used for engineering applications. However, the sampling protocol used by the satellite leads to some difficulties in making use of these data for practical applications. These difficulties and techniques to estimate extreme wave heights using satellite measurements are discussed. Significant wave heights for a 50-year return period are estimated using GEOSAT measurements for several regions around North America. Techniques described here may be used for estimation of wave heights associated with any specified return interval in regions where buoy data are not readily available.     

基于广义极值分布的设计波高推算

简介了广义极值分布函数及其3种参数估计方法,包括极大似然(ML)、线性矩(LM)和间隔最大积(MPS)估计的计算方法。使用广义极值分布函数推算了北部湾涠洲岛海域3个波向的年波高极值序列设计波高,并与Weibull分布、Gumbel分布和皮尔逊Ⅲ型分布的推算结果加以对比。分析表明,涠洲岛海域极值波高服从于广义极值Ⅲ型分布,拟合优度检验结果表明广义极值分布能更好地拟合极值波高;MPS方法是一种优良的参数估计法,推算的设计波高可作为海岸环境工程设计的首要参考值。     

Prediction of significant wave height using regressive support vector machines

Wave parameters prediction is an important issue in coastal and offshore engineering. In this literature, several models and methods are introduced. In the recent years, the well-known soft computing approaches, such as artificial neural networks, fuzzy and adaptive neuro-fuzzy inference systems and etc., have been known as novel methods to form intelligent systems, these approaches has also been used to predict wave parameters, as well. It is not a long time that support vector machine (SVM) is introduced as a strong machine learning and data mining tool. In this paper, it is used to predict significant wave height (H_s). The data set used in this study comprises wave wind data gathered from deep water locations in Lake Michigan. Current wind speed (u) and those belonging up to six previous hours are given as input variables, while the significant wave height is the output parameter. The SVM results are compared with those of artificial neural networks, multi-layer perceptron (MLP) and radial basis function (RBF) models. The results show that SVM can be successfully used for prediction of H_s. Furthermore, comparisons indicate that the error statistics of SVM model marginally outperforms ANN even with much less computational time required.     

Prediction of maximum wave-induced liquefaction in porous seabed using multi-artificial neural network model

In the last few decades, considerable efforts have been devoted to the phenomenon of wave-induced liquefactions, because it is one of the most important factors for analysing the seabed and designing marine structures. Although numerous studies of wave-induced liquefaction have been carried out, comparatively little is known about the impact of liquefaction on marine structures. Furthermore, most previous researches have focused on complicated mathematical theories and some laboratory work. In the present study, a data dependent approach for the prediction of the wave-induced liquefaction depth in a porous seabed is proposed, based on a multi-artificial neural network (MANN) method. Numerical results indicate that the MANN model can provide an accurate prediction of the wave-induced maximum liquefaction depth with 10% of the original database. This study demonstrates the capacity of the proposed MANN model and provides coastal engineers with another effective tool to analyse the stability of the marine sediment.     

Soft computing approach for real-time estimation of missing wave heights

This paper presents soft computing approach for estimation of missing wave heights at a particular location on a real-time basis using wave heights at other locations. Six such buoy networks are developed in Eastern Gulf of Mexico using soft computing techniques of Artificial Neural Networks (ANN) and Genetic Programming (GP). Wave heights at five stations are used to estimate wave height at the sixth station. Though ANN is now an established tool in time series analysis, use of GP in the field of time series forecasting/analysis particularly in the area of Ocean Engineering is relatively new and needs to be explored further. Both ANN and GP approach perform well in terms of accuracy of estimation as evident from values of various statistical parameters employed. The GP models work better in case of extreme events. Results of both approaches are also compared with the performance of large-scale continuous wave modeling/forecasting system WAVEWATCH III. The models are also applied on real time basis for 3 months in the year 2007. A software is developed using evolved GP codes (C++) as back end with Visual Basic as the Front End tool for real-time application of wave estimation model.     

Comparison of linear and nonlinear extreme wave statistics

An extremely large (“freak”) wave is a typical though rare phenomenon observed in the sea. Special theories (for example, the modulation instability theory) were developed to explain mechanics and appearance of freak waves as a result of nonlinear wave-wave interactions. In this paper, it is demonstrated that the freak wave appearance can be also explained by superposition of linear modes with the realistic spectrum. The integral probability of trough-to-crest waves is calculated by two methods: the first one is based on the results of the numerical simulation of a wave field evolution performed with one-dimensional and two-dimensional nonlinear models. The second method is based on calculation of the same probability over the ensembles of wave fields constructed as a superposition of linear waves with random phases and the spectrum similar to that used in the nonlinear simulations. It is shown that the integral probabilities for nonlinear and linear cases are of the same order of values     

Probability distributions of wave heights in bimodal seas in an offshore basin

This study aims at assessing the adequacy for describing bimodal sea states of different non-linear probability distributions that have been developed for single sea states. It is based on data collected at an offshore test basin. The measurements represent three bimodal sea states with individual unidirectional wave systems propagating at 60, 90 and 120 from each other. The wave spectra are separated into swell and wind sea components and the relative energy ratio between the areas under the associated spectral curves is estimated and is related with the statistics of the time series considered. Dependence is found between the normalized high order cumulants, which describe the non-Gaussian surface, and the predominant contribution of the wind sea energy. Furthermore, the probabilities of exceedance of the individual wave heights are estimated and compared with the Rayleigh model and with other models that take into account either the effect of spectral bandwidth or the effect of wave nonlinearities. The results are discussed with respect to three classes of sea states that reflect the relative contribution of swell and wind sea energy.     

太平洋SSTA同中国东部夏季极端降水事件变化关系的研究

基于中国东部1955—2004年233个台站逐日降水和NCEP/NCAR再分析高度场、风场、比湿、地面气压以及NOAA海表温度资料,运用SVD、合成分析等方法研究了太平洋SSTA同中国东部夏季极端降水事件之间的相互关系,结果表明:前期冬季太平洋SSTA同我国东部夏季极端降水事件的关系比较显著;前冬赤道中东太平洋是影响我国华北地区夏季极端降水事件的关键区,若前冬该海域海温发生异常,从冬到夏大气环流先后通过PNA(反PNA)和WP(反WP)遥相关型使得西太平洋副热带高压发生异常,进而使得华北夏季极端降水事件发生异常;前冬热带西太平洋是影响我国东北南部及江南地区夏季极端降水事件的关键区,若前冬该海域海温发生异常,从冬到夏105°~135°E的平均经向垂直环流圈发生异常,使得夏季东北南部与江南地区垂直运动发生异常,进而使得东北南部和江南夏季极端降水事件发生异常。     

极值波高与历时联合分布的重现水平分析

使用美国北卡罗来纳州的 FRF 1985—2016 年的极值波高及其持续时间数据,采用最优的 Gumbel-Hougaard copula函数和 Kendall 分布函数构建极值波高和相应历时不同组合的联合概率分布模式,分析各个组合的遭遇概率、“或”重现期、“且”重现期和 Kendall 重现期,以出现最大可能概率的方法推算各组合联合设计值。结果表明：Kendall 重现期所对应的累积频率更准确地代表了特定设计频率下的风险率;重现期分别为 5 年、10 年、20 年、50 年、100 年、200 年推算的 Kendall重现期设计值介于“或”重现期和“且”重现期设计值之间,小于相应的边缘分布设计值;基于 Kendall 重现期的极值波高及其持续时间不同重现期组合推算的结果可为海洋工程构筑物设计与风险管理提供新的选择与参考。     

运用SWAN数值模拟对渤海波浪极值参数的研究(英文)

本文以高分辨率后报风场资料为输入,采用SWAN波浪模式,模拟了渤海海域1985年至2004年共20年间的波浪场。通过有效波高数据的比较,可看出波浪数值结果与实测资料符合较好,可以用数值结果分析渤海海域的波浪特征。利用计算的年极值波要素,本文给出并分析了渤海海域不同重现期下的极值参数分布情况。     

Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea

In multi-resolution analysis (MRA) by wavelet function Daubechies (db), we decompose the signal in two parts, the low and high-frequency contents. We remove the high-frequency content and reconstruct a new “de-noise” signal by using inverse wavelet transform. The calculation of tidal constituent phase-lags was made to determine the input and output data patterns used in building network structure of Artificial Neuron-Network (ANN) model. The “de-noise” signal was, then, used as the input data to improve the forecasting accuracy of the ANN model. The wavelet spectrum, conventional energy spectrum (fast Fourier transform, FFT), and harmonic analysis were used to analyze the characteristics of tidal data.Using only a very short-period data as a training data set in Artificial Neuron-Network Back-Propagate (ANN-BP) model, the developed ANN+Wavelet model can accurately predict or supply the missing tide data for a long period (1–5 years). The results also show that the concept of tidal constituent phase-lags can improve ANN model of tidal forecasting and data supplement. The addition of the wavelet analysis to ANN method can prominently improve the prediction quality.     

Correlation of extreme waves and water levels using a third-generation wave model and a 3D flow model

A procedure to correlate extreme wave heights and extreme water levels in coastal waters using numerical models together with joint probability analysis has been proposed. A third-generation wave model for wave simulation and a three-dimensional flow model for water level simulation are coupled through the surface atmospheric boundary layer. The model has been calibrated and validated against wind, wave and water level data collected in the coastal waters of Hong Kong. The annual maximum wave height and the concomitant water level have been obtained by simulating the annual extreme typhoon event for 50 consecutive years. The results from bivariate extreme value analysis of the simulated data show that the commonly used empirical method may lead to underestimation of the design water level.     

青岛近岸能见度逐小时分级预报模型初探

本文对青岛2014~2015年4~8月份位于青岛奥帆基地站和胶州湾跨海大桥上的大桥4站的逐小时自动气象站测量数据进行多种统计分析工作,发现两地能见度变化影响因子有不同特点,大桥4站在雾出现时降温增湿过程更明显。能见度处于1km以下时,风速降低,温度降低,绝对湿度减小会导致更低的能见度出现。使用神经网络方法分别对两站建立逐小时的能见度分级预报模型,并用2016年4~8月数据进行预报检验,表明少数关键预报因子就可以较好地建立预报模型,奥帆基地站能见度0~1km和1~5km级别的TS评分可超过0.4,因子增多会提升能见度1~5km级别的预报效果。两站独立建模比使用一个模型预报效果更好。该研究为青岛近岸能见度精细化客观定量预报提供参考。     

Prediction of geometrical properties of perfect breaking waves on composite breakwaters

Breaking wave loads on coastal structures depend primarily on the type of wave breaking at the instant of impact. When a wave breaks on a vertical wall with an almost vertical front face called the “perfect breaking”, the greatest impact forces are produced. The correct prediction of impact forces from perfect breaking of waves on seawalls and breakwaters is closely dependent on the accurate determination of their configurations at breaking. The present study is concerned with the determination of the geometrical properties of perfect breaking waves on composite-type breakwaters by employing artificial neural networks. Using a set of laboratory data, the breaker crest height, h_b, breaker height, H_b, and water depth in front of the wall, d_w, from perfect breaking of waves on composite breakwaters are predicted using the artificial neural network technique and the results are compared with those obtained from linear and multi-linear regression models. The comparisons of the predicted results from the present models with measured data show that the h_b, H_b and d_w values, which represent the geometry of waves breaking directly on composite breakwaters, can be predicted more accurately by artificial neural networks compared to linear and multi-linear regressions.     

Evaluation of the applicability of pier local scour formulae using laboratory and field data

The reliable estimation of the local scour depth at a bridge pier is essential for proper design and maintenance of bridge piers. Most local scour formulae have been developed based on the results of laboratory experiments. The formulae based on laboratory data do not often produce reasonable predictions for field piers because laboratory investigations are apt to oversimplify or ignore many of the complexities of the flow fields around the bridge piers. Validation of the formulae is necessary in order to ascertain which of the formulae are able to provide reasonable estimates of the local scour depth. In this study, six commonly cited formulae based on laboratory data or field data were selected for validation using 180 laboratory data sets gathered from the literature and 446 field data sets collected from four countries. The six formulae validated in this paper are the Colorado State University (CSU), Neill, Froehlich, Breuser, Laursen, and simplified Chinese formulae. Comparisons between the predicted and measured depths were performed using scour from the laboratory and field data. An artificial neural network technique was also applied in order to compare the tendencies between the field and laboratory data sets.     

Modelling the cross-shore profiles of sand beaches using artificial neural networks

Abstract

Artificial neural networks (ANN) have been widely used successfully to solve coastal engineering problems. In this article, they are used to model the cross-shore profile of sandy beaches taking into account the possible effect of marine vegetation (Posidonia oceanica). Sixty ANNs were generated by modifying both the inputs and the number of neurons in the hidden layer. The best results were obtained with the following inputs: wave height perpendicular to the coast and the associated period and probability of occurrence, median sediment size, profile slope, and energy reduction factor due to P. oceanica. With these inputs and 10 neurons in the hidden layer, a mean absolute error of 0.22?m during training and 0.21?m during the test was obtained, which represents an improvement of 81.2% and 55.5% compared to models without and with P. oceanica.     

基于灰色多元变权组合预测模型对山东省海水养殖产量预测

为了进一步提高海水养殖产量预测精度, 考虑多因素对海水养殖产量的影响, 文章基于变权组合预测模型, 充分结合长短期记忆(Long Short-Term Memory, LSTM)神经网络预测模型、GM(1, N)预测模型和偏最小二乘回归预测模型等传统统计预测模型的优点, 构建出一种基于灰色多元变权组合预测模型, 并对山东省海水养殖总产量和分类产量进行了预测。实证结果显示, 基于灰色多元变权组合预测模型对山东省海水养殖产量的预测精度高达99.13%, 预测精度显著优于LSTM神经网络等各单项模型, 并能综合于LSTM神经网络、GM(1, N)预测模型和偏最小二乘回归预测模型的优点, 弥补各单项模型的不足, 提高预测精度和可靠性。根据预测结果, 到2025年山东省海水养殖产量仍将保持良好发展, 海水养殖产量将达到579.28×10⁵t, 平均增长速度为3.11%, 而鱼类、甲壳类、贝类、藻类以及海参海水养殖产量将分别达到6.27×10⁵、26.27×10⁵、445.83×10⁵、68.65×10⁵和9.5×10⁵t。     

Estimating wave crest distributions using the method of L-moments

The design of fixed or floating offshore structures requires accurate information of the met-ocean data at the intended offshore site. In the design process it is recognized that this environmental data is modified in the near-field by the interaction with the particular geometrical configuration of the offshore structure. This transformation of the incident wave field around and beneath an offshore structure presents a challenge for ocean engineers when specifying the wave gap elevation to avoid impact loads on the underside of the deck and inundation of the topsides. Thus, the accurate estimation of the wave crest distributions from measurements at various locations near and under the offshore structure during model test studies is essential. A semi-empirical approach is presented herein that builds upon the findings of previous studies and introduces the Method of L-moments. A three parameter model for a wave crest probability distribution function is presented and explicit relationships between the parameters of the distribution and its’ first three L-Moments are established. Furthermore, three narrow-band models from earlier research studies are reviewed and compared with the new model. Wave measurements from a mini-TLP model test program are used as the basis for comparison of the four distributions. The root-mean-square error is used as a metric to quantify the overall fit of the data and its accuracy in the high end tail of the data. The L-Moment model is shown to be more robust in representing the data in both the far-field and beneath the deck of the mini-TLP where the wave field demonstrates increased non-linear behavior.     

浮游植物密度的人工神经网络预测研究

将人工神经网络模型应用于藻类密度数据的预测计算之中,并利用遗传算法对其网络结构进行优化计算以保证计算结果的准确性且自动确定网络结构,分别对神经网络的“当天模型”和“预测模型”进行了计算。结果显示：几种人工神经网络模型在计算精度以及预测数据的趋势上都有较好的效果,目前国内学者使用的人工神经网络“当天模型”无法对其后数据进行预测,不能起到实际预测的作用,而经过遗传算法优化后的人工神经网络“预测模型”不仅达到了很好的预测效果,而且网络结构简单,适用于浮游植物密度的预测计算。