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1.
Forecasting of wave parameters is necessary for many marine and coastal operations. Different forecasting methodologies have been developed using the wind and wave characteristics. In this paper, artificial neural network (ANN) as a robust data learning method is used to forecast the wave height for the next 3, 6, 12 and 24 h in the Persian Gulf. To determine the effective parameters, different models with various combinations of input parameters were considered. Parameters such as wind speed, direction and wave height of the previous 3 h, were found to be the best inputs. Furthermore, using the difference between wave and wind directions showed better performance. The results also indicated that if only the wind parameters are used as model inputs the accuracy of the forecasting increases as the time horizon increases up to 6 h. This can be due to the lower influence of previous wave heights on larger lead time forecasting and the existing lag between the wind and wave growth. It was also found that in short lead times, the forecasted wave heights primarily depend on the previous wave heights, while in larger lead times there is a greater dependence on previous wind speeds. 相似文献
2.
Prediction of wave parameters by using fuzzy logic approach 总被引：2，自引：0，他引：2
The purpose of this study is to investigate the relationship between wind speed, previous and current wave characteristics. It is expected that such a non-linear relationship includes some uncertainties. A fuzzy inference system employing fuzzy IF–THEN rules has an ability to deal with ill-defined and uncertain systems. Compared with traditional approaches, fuzzy logic is more efficient in linking the multiple inputs to a single output in a non-linear domain. In this paper, a sophisticated intelligent model, based on Takagi–Sugeno (TS) fuzzy modeling principles, was developed to predict the changes in wave characteristics such as significant wave height and zero up-crossing period due to the wind speed. Past measurements of significant wave height values and wind speed variables are used for training the adaptive model and it is then employed to predict the significant wave height amounts for future time intervals such as 1, 3, 6 and 12 h. The verification of the proposed model is achieved through the wave characteristics time series plots and various numerical error criterias. Also the model results were compared with classical Auto Regressive Moving Average with exogenous input (ARMAX) models. For the application of the proposed approach the offshore station located in the Pacific Ocean was used. 相似文献
3.
Precise prediction of extreme wave heights is still an evading problem whether it is done using physics based modeling or by extensively used data driven technique of Artificial Neural Network (ANN). In the present paper, Neuro Wavelet Technique (NWT) is used specifically to explore the possibility of prediction of extreme events for five major hurricanes Katrina 2005, Dean 2007, Gustav 2008, Ike 2008, Irene 2011 at four locations (NDBC wave buoys stations)1 namely; 42040, 42039, 41004, 41041 in the Gulf of Mexico. Neuro Wavelet Technique is employed by combining Discrete Wavelet Transform and Artificial Neural Networks. Discrete wavelet transform analyzes frequency of signal with respect to time at different scales. It decomposes time series into low (approximate) and high (detail) frequency components. The decomposition of approximate components (extreme events in the ocean wave series) can be carried out up to the desired multiple levels in order to provide relatively smooth varying amplitude series. This feature of wavelet transforms make it plausible for predicting extreme events with a better accuracy. In the present study third, fifth and seventh level of decompositions are used which facilitates 3 to 7 times filtering of low frequency events and seems to pay the dividend in the form of better prediction accuracy at extreme events. To develop these Neuro wavelet models to forecast the waves with lead times of 12 hr to 36 hr in advance, previously measured significant wave heights at same locations were used. The results were judged by wave plots, scatter plots and other error measures. From the results it can be concluded that the Neuro Wavelet Technique can be employed to solve the ever eluding problem of accurate forecasting of the extreme events. 相似文献
4.
L. Vandenbulcke J.-M. Beckers A. Barth M. Aidonidis F. Ardhuin C. Fratianni D. Pallela M. Tudor P. Martin M. Rixen 《Progress in Oceanography》2009,82(3):149-167
The prediction of surface drift of floating objects is an important task, with applications such as marine transport, pollutant dispersion, and search-and-rescue activities. But forecasting even the drift of surface waters is very challenging, because it depends on complex interactions of currents driven by the wind, the wave field and the general prevailing circulation. Furthermore, although each of those can be forecasted by deterministic models, the latter all suffer from limitations, resulting in imperfect predictions. In the present study, we try and predict the drift of two buoys launched during the DART06 (Dynamics of the Adriatic sea in Real-Time 2006) and MREA07 (Maritime Rapid Environmental Assessment 2007) sea trials, using the so-called hyper-ensemble technique: different models are combined in order to minimize departure from independent observations during a training period; the obtained combination is then used in forecasting mode. We review and try out different hyper-ensemble techniques, such as the simple ensemble mean, least-squares weighted linear combinations, and techniques based on data assimilation, which dynamically update the model’s weights in the combination when new observations become available. We show that the latter methods alleviate the need of fixing the training length a priori, as older information is automatically discarded.When the forecast period is relatively short (12 h), the discussed methods lead to much smaller forecasting errors compared with individual models (at least three times smaller), with the dynamic methods leading to the best results. When many models are available, errors can be further reduced by removing colinearities between them by performing a principal component analysis. At the same time, this reduces the amount of weights to be determined.In complex environments when meso- and smaller scale eddy activity is strong, such as the Ligurian Sea, the skill of individual models may vary over time periods smaller than the forecasting period (e.g. when the latter is 36 h). In these cases, a simpler method such as a fixed linear combination or a simple ensemble mean may lead to the smallest forecast errors. In environments where surface currents have strong mean-kinetic energies (e.g. the Western Adriatic Current), dynamic methods can be particularly successful in predicting the drift of surface waters. In any case, the dynamic hyper-ensemble methods allow to estimate a characteristic time during which the model weights are more or less stable, which allows predicting how long the obtained combination will be valid in forecasting mode, and hence to choose which hyper-ensemble method one should use. 相似文献
5.
A.J.H.M. Reniers M.J. Groenewegen K.C. Ewans S. Masterton G.S. Stelling J. Meek 《Coastal Engineering》2010
The accuracy of nearshore infragravity wave height model predictions has been investigated using a combination of the spectral short wave evolution model SWAN and a linear 1D SurfBeat model (IDSB). Data recorded by a wave rider located approximately 3.5 km from the coast at 18 m water depth have been used to construct the short wave frequency-directional spectra that are subsequently translated to approximately 8 m water depth with the third generation short wave model SWAN. Next the SWAN-computed frequency-directional spectra are used as input for IDSB to compute the infragravity response in the 0.01 Hz–0.05 Hz frequency range, generated by the transformation of the grouped short waves through the surf zone including bound long waves, leaky waves and edge waves at this depth. Comparison of the computed and measured infragravity waves in 8 m water depth shows an average skill of approximately 80%. Using data from a directional buoy located approximately 70 km offshore as input for the SWAN model results in an average infragravity prediction skill of 47%. This difference in skill is in a large part related to the under prediction of the short wave directional spreading by SWAN. Accounting for the spreading mismatch increases the skill to 70%. Directional analyses of the infragravity waves shows that outgoing infragravity wave heights at 8 m depth are generally over predicted during storm conditions suggesting that dissipation mechanisms in addition to bottom friction such as non-linear energy transfer and long wave breaking may be important. Provided that the infragravity wave reflection at the beach is close to unity and tidal water level modulations are modest, a relatively small computational effort allows for the generation of long-term infragravity data sets at intermediate water depths. These data can subsequently be analyzed to establish infragravity wave height design criteria for engineering facilities exposed to the open ocean, such as nearshore tanker offloading terminals at coastal locations. 相似文献
6.
7.
The literature on ocean wave forecasting falls into two categories, physics-based models and statistical methods. Since these two approaches have evolved independently, it is of interest to determine which approach can predict more accurately, and over what time horizons. This paper runs a comparative analysis of a well-known physics-based model for simulating waves near shore, SWAN, and two statistical techniques, time-varying parameter regression and a frequency domain algorithm. Forecasts are run for the significant wave height, over horizons ranging from the current period (i.e., the analysis time) to 15 h. Seven data sets, four from the Pacific Ocean and three from the Gulf of Mexico, are used to evaluate the forecasts. The statistical models do extremely well at short horizons, producing more accurate forecasts in the 1–5 hour range. The SWAN model is superior at longer horizons. The crossover point, at which the forecast error from the two methods converges, is in the area of 6 h. Based on these results, the choice of statistical versus physics-based models will depend on the uses to which the forecasts will be put. Utilities operating wave farms, which need to forecast at very short horizons, may prefer statistical techniques. Navies or shipping companies interested in oceanic conditions over longer horizons will prefer physics-based models. 相似文献
8.
Fourier transform (FT) is a commonly used method in spectral analysis of ocean wave and offshore structure responses,but it is not suitable for records of short length.In this paper another method,wavelet transform (WT),is applied to analyze the data of short length.The Morlet wavelet is employed to calculate the spectral density functions for wave records and simulated Floating Production Storage and Offloading (FPSO) vessels' responses.Computed wave data include simulated wave data based on JONSWAP spectr... 相似文献
9.
介绍采用谱分析原理提取海浪信息的方法。根据GPS浮标测量得到的海浪高度数据,先通过滑动平均的方法分离海浪信息和潮流潮位信息,然后对海浪信息采用AR模型法进行功率谱估计,并对海浪信息进行连续小波变换,对AR模型法功率谱估计结果和小波变换结果进行综合研究,提取海浪特征参数———周期。最后通过一个GPS浮标试验对上述方法进行了验证。 相似文献
10.
An analytical solution is undertaken to describe the wave-induced flow field and the surge motion of a permeable platform structure with fuzzy controllers in an oceanic environment. In the design procedure of the controller, a parallel distributed compensation (PDC) scheme is utilized to construct a global fuzzy logic controller by blending all local state feedback controllers. A stability analysis is carried out for a real structure system by using Lyapunov method. The corresponding boundary value problems are then incorporated into scattering and radiation problems. They are analytically solved, based on separation of variables, to obtain series solutions in terms of the harmonic incident wave motion and surge motion. The dependence of the wave-induced flow field and its resonant frequency on wave characteristics and structure properties including platform width, thickness and mass has been thus drawn with a parametric approach. From which mathematical models are applied for the wave-induced displacement of the surge motion. A nonlinearly inverted pendulum system is employed to demonstrate that the controller tuned by swarm intelligence method can not only stabilize the nonlinear system, but has the robustness against external disturbance. 相似文献
11.
本文提出一种基于支持向量回归的统计预报方法,通过经验正交分解对原始数据矩阵进行时空分解,提取出空间模态和时间系数。由于海面高度变化具有非线性、大惯性的特点,对时间系数进行小波分析,能有效滤除其中的高频信号,得到表征海面高度变化的低频信号。利用支持向量回归方法对小波分解后的低频信号构建预报模型。最后,进行小波重构,还原时间序列长度,实现未来7天的海面高度预报。通过黑潮附近海域的海面高度预报结果验证,该预报方法的预报效果优于整合滑动平均自回归预报方法。本文通过机器学习的算法实现了海面高度的预报,为海洋预报方法提供了新的思路。 相似文献
12.
In this paper, first we introduce the wave run-up scale which describes the degree of wave run-up based on observed sea conditions near and on a coastal structure. Then, we introduce a simple method which can be used for daily forecast of wave run-up on a coastal structure. The method derives a multiple linear regression equation between wave run-up scale and offshore wind and wave parameters using long-term photographical observation of wave run-up and offshore wave forecasting model results. The derived regression equation then can be used for forecasting the run-up scale using the offshore wave forecasting model results. To test the implementation of the method, wave run-up scales were observed at four breakwaters in the East Coast of Korea for 9 consecutive months in 2008. The data for the first 6 months were used to derive multiple linear regression equations, which were then validated using the run-up scale data for the remaining 3 months and the corresponding offshore wave forecasting model results. A comparison with an engineering formula for wave run-up is also made. It is found that this method can be used for daily forecast and warning of wave run-up on a coastal structure with reasonable accuracy. 相似文献
13.
《Coastal Engineering》2005,52(3):221-236
The notion of data assimilation is common in most wave predictions. This typically means nudging of wave observations into numerical predictions so as to drive the predictions towards the observations. In this approach, the predicted wave climate is corrected at each time of the observation. However, the corrections would diminish soon in the absence of future observations. To drive the model state predictions towards real time climatology, the updating has to be carried out in the forecasting horizon too. This could be achieved if the wave forecasting at the observational network is made available. The present study addresses a wave forecasting technique for a discrete observation station using local models. Embedding theorem based on the time-lagged embedded vector is the basis for the local model. It is a powerful tool for time series forecasting. The efficiency of the forecasting model as an error correction tool (by combining the model predictions with the measurements) has been brought up in a forecasting horizon from few hours to 24 h. The parameters driving the local model are optimised using evolutionary algorithms. 相似文献
14.
In this paper, we define a time-domain pressure transfer function calculated from SIWEH (smoothed instantaneous wave energy history) transforms, and a time-frequency domain pressure transfer function calculated from wavelet transforms, of synchronized wave and pressure data. It is our objective to study whether the time-domain pressure transfer function and the time-frequency domain pressure transfer function can provide new interpretation of wind wave behaviors. The detail structure of local time-frequency pressure transfer function in three-dimensional plot from wavelet transform is not employed due to its large variations, instead the time-integral wavelet spectral pressure transfer function and frequency-integral wavelet SIWEH pressure transfer function are used. These two averaged pressure transfer functions are smooth approximations of frequency-domain Fourier and time-domain SIWEH pressure transfer functions, respectively.Application to real ocean waves reveals that in frequency-domain the measured Fourier and wavelet spectral pressure transfer functions can be approximated by the linear pressure transfer function in the dominant wave range. In time-domain, the wavelet SIWEH pressure transfer function is a better indicator of wind wave behaviors than the SIWEH pressure transfer function. A value higher than 0.5 for the wavelet SIWEH pressure transfer function is a good discriminator of relative shallow-water long waves and wave groups are mostly composed of relative low frequency long waves. 相似文献
15.
Real-time wave forecasting using genetic programming 总被引：4，自引：0，他引：4
The forecasting of ocean waves on real-time or online basis is necessary while carrying out any operational activity in the ocean. In order to obtain forecasts that are station-specific a time-series-based approach like stochastic modeling or artificial neural network was attempted by some investigators in the past. This paper presents an application of a relatively new soft computing tool called genetic programming for this purpose. Genetic programming is an extension of genetic algorithm and it is suited to explore dependency between input and output data sets. The wave rider buoy measurements available at two locations in the Gulf of Mexico are analyzed. The forecasts of significant wave heights are made over lead times of 3, 6, 12 and 24 h. The sample size belonged to a period of 15 years and it included an extensive testing period of 5 years. The forecasts made by the approach of genetic programming indicated that it can be regarded as a promising tool for future applications to ocean predictions. 相似文献
16.
Wave induced excess flow of momentum(WIEFM)is the averaged flow of momentum over a wave period due to wave presence,which may also be called 3-D radiation stress.In this paper,the 3-D current equations with WIEFM are derived from the averaged Navier-Stokes equations over a wave period,in which the velocity is separated into the large-scale background velocity,the wave particle velocity and the turbulent fluctuation velocity.A concept of wave fluctuating layer(WFL)is put forward,which is the vertical column from the wave trough to wave ridge.The mathematical expressions of WIEFM in WFL and below WFL are given separately.The parameterized expressions of WIEFM are set up according to the linear wave theory.The integration of WIEFM in the vertical direction equals the traditional radiation stress(namely 2-D radiation stress)given by Longuet-Higgins and Stewart. 相似文献
17.
针对新一代天气雷达数据存在风轮机杂波污染问题,统计分析了5种雷达基数据特征量,风轮机杂波具有较高的反射率因子隆起度和水平通道信噪比隆起度以及较大的速度奇异率,其信号质量指数接近1,谱宽接近0或大于7.0 m·s-1。以特征量统计结果为基础,使用模糊逻辑算法对雷达基数据中的风轮机杂波进行特征识别,结果显示,模糊逻辑算法能有效地把风轮机杂波从各种强度的气象降水回波和固定地物杂波中识别出来。针对被识别出来的风轮机杂波,使用区域平均插值方法对雷达基数据中的风轮机杂波进行剔除,杂波剔除结果显示,气象降水条件下获得了较好的风轮机杂波剔除效果。 相似文献
18.
Recently, the technology has been developed to make wave farms commercially viable. Since electricity is perishable, utilities will be interested in forecasting ocean wave energy. The horizons involved in short-term management of power grids range from as little as a few hours to as long as several days. In selecting a method, the forecaster has a choice between physics-based models and statistical techniques. A further idea is to combine both types of models. This paper analyzes the forecasting properties of a well-known physics-based model, the European Center for Medium-Range Weather Forecasts (ECMWF) Wave Model, and two statistical techniques, time-varying parameter regressions and neural networks. Thirteen data sets at locations in the Atlantic and Pacific Oceans and the Gulf of Mexico are tested. The quantities to be predicted are the significant wave height, the wave period, and the wave energy flux. In the initial tests, the ECMWF model and the statistical models are compared directly. The statistical models do better at short horizons, producing more accurate forecasts in the 1-5 h range. The ECMWF model is superior at longer horizons. The convergence point, at which the two methods achieve comparable degrees of accuracy, is in the area of 6 h. By implication, the physics-based model captures the underlying signals at lower frequencies, while the statistical models capture relationships over shorter intervals. Further tests are run in which the forecasts from the ECMWF model are used as inputs in regressions and neural networks. The combined models yield more accurate forecasts than either one individually. 相似文献
19.
Christopher J. Hulatt David N. ThomasDavid G. Bowers Louiza NormanChi Zhang 《Estuarine, Coastal and Shelf Science》2009
The quantity of chromophoric or coloured dissolved organic matter (CDOM) released by eleven species of intertidal and sub-tidal macroalgae commonly found on UK shores was investigated. The subsequent breakdown of CDOM was also measured by exposing collected CDOM samples to light and dark conditions for over two weeks. CDOM absorption properties were compared at a fixed wavelength of 440 nm and across two integrated wave - bands; UV-A (400–315 nm) and UV-B (315–280 nm). Absorption spectra of macroalgal CDOM samples were typically characterized by peaks and shoulders in the UV bands, features which were species specific. The spectral slope, derived using the log-linear method, proved to be very specific to the species and to the effect of light. Slope measurements ranged from 0.010 to 0.027 nm−1, in the range of normal seawater values. Significantly more CDOM was produced by algae which were illuminated, providing evidence for a light driven exudation mechanism. Averaged across all species, exudation in the dark accounted for 63.7% of that in the light in the UV-B band. Interspecific differences in exudation rate encompassed an order of magnitude, with the highest absorption measurements attributable to brown algae. However, some brown algae produced considerably less CDOM (e.g. Pelvetia canaliculata), which were more comparable to the green and red species. Over an exposure time of 16 days, significant photochemical degradation of CDOM was observed using a natural summer sunlight regime, showing that natural solar radiation could be an important removal mechanism for newly produced algal CDOM. Though the most obvious effect was a decrease in absorption, photo-bleaching also caused a significant increase in the spectral slope parameter of 0.004 nm−1. 相似文献
20.
随着海南深水网箱养殖规模的不断扩大,海浪精细化预报的需求越来越紧迫。以海南岛周边海域为目标区域,基于近岸海洋模式ADCIRC(Advancedcirculationmodel)和海浪模式SWAN(Simulating WavesNearshore),建立了海南岛近岸养殖区台风浪数值预报系统。该系统采用非结构高分辨率网格,近岸分辨率达到了100m。选取2014年第9号超强台风"威马逊"(RAMMASUN)进行针对海南岛近岸养殖区的台风浪数值模拟后报。模拟结果与实测数据较为吻合。采用全球预报系统GFS(Global Forecast System)风场和气压场数据作为驱动场对2018年7月的一次热带风暴过程进行预报,48小时、24小时预报的有效波高和实测结果比较平均相对误差分别为20.75%和17.0%。总体来说,该模型的预报精度可以满足近岸养殖区台风浪预报业务的需求。 相似文献