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1.
Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modeling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired. 相似文献
2.
Mehmet Özger 《Ocean Engineering》2010,37(16):1443-1451
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. 相似文献
3.
《Coastal Engineering》2007,54(9):643-656
This paper aims at improving the prediction of wave transmission behind low-crested breakwaters by means of a numerical model based on Artificial Neural Networks (ANNs). The data here used are those gathered within the European research project DELOS.Firstly, the motivations that lead to employ an ANN numerical model to forecast the wave transmission behind low-crested structures are discussed. Then, the ANN model is tested and its architecture is optimized with a test targeted on assessing both the accuracy and the robustness of the method. A study is devoted to investigate the ANN model capability in reproducing some physical relationships among the involved parameters. Finally, comparisons of ANN results with those from experimental formulations based on the classic regression approach demonstrate a considerable improvement in the forecast accuracy.The ANN forecasting tool is available as a user-friendly Internet applet at: http://w3.uniroma1.it/cmar/wave_transm_kt.htm. 相似文献
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5.
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. 相似文献
6.
针对海洋中的海浪高度数据存在非线性和非平稳性的特点,海浪高度的预测就变得相对复杂。基于变分模态分解(VMD),在引入注意力机制(AM)的基础上,对传统长短期记忆(LSTM)神经网络算法进行了改进,提出了一种基于混合模型的海浪高度预测算法。算法通过预处理、预测和重构3个主要步骤,对海浪高度的时间序列进行预测。为了比较和说明,以太平洋东北海盆海域和马尾藻海域的4个站点浮标数据进行实验。实验结果表明,本文提出的混合模型(VALM)将海浪高度数据分解为更平稳和更规则的子序列;可以更好的区分数据之间的重要程度,并能够携带更多信息的数据;与支持向量回归(SVR)、人工神经网络(ANN)和LSTM等模型进行比较,VALM模型的预测效果最好且具备一定的鲁棒性。 相似文献
7.
An artificial neural network (ANN) was applied to predict seasonal beach profile evolution at various locations along the Tremadoc Bay, eastern Irish Sea. The beach profile variations in 19 stations for a period of about 7 years were studied using ANN. The model results were compared with field data. The most critical part of constructing ANN was the selection of minimum effective input data and the choice of proper activation function. Accordingly, some numerical techniques such as principal component analysis and correlation analysis were employed to detect the proper dataset. The geometric properties of the beach, wind data, local wave climate, and the corresponding beach level changes were fed to a feedforward backpropagation ANN. The performance of less than 0.0007 (mean square error) was achieved. The trained ANN model results had very good agreement with the beach profile surveys for the test data. Results of this study show that ANN can predict seasonal beach profile changes effectively, and the ANN results are generally more accurate when compared with computationally expensive mathematical model of the same study region. The ANN model results can be improved by the addition of more data, but the applicability of this method is limited to the range of the training data. 相似文献
8.
Unlike in the open sea, the use of wind information for forecasting waves may encounter more ambiguous uncertainties in the coastal or harbor area due to the influence of complicated geometric configurations. Thus this paper attempts to forecast the waves based on learning the characteristics of observed waves, rather than the use of the wind information. This is reported in this paper by the application of the artificial neural network (ANN), in which the back-propagation algorithm is employed in the learning process for obtaining the desired results. This model evaluated the interconnection weights among multi-stations based on the previous short-term data, from which a time series of waves at a station can be generated for forecasting or data supplement based on using the neighbor stations data. Field data are used for testing the applicability of the ANN model. The results show that the ANN model performs well for both wave forecasting and data supplement when using a short-term observed wave data. 相似文献
9.
1 .IntroductionTheartificialneuralnetwork(ANN)hasbeenwidelyusedinmanyscientificfieldsinrecentyears .Itisakindofinformationmanagementsystemthatresemblesthehumanbraininworkpattern .Comparedwiththetraditionalmethodsofnumericalsimulation ,ANNhastheadvantagesofrelativein dependenceofphysicalmodel,uniformandsimplewayofrealization ,quicknessofcomputing ,andsoon .Sincethemodelofartificialneuronswasfirstlyintroducedin 1 943,ithasbeendevelopedthroughseveralstages.TheapplicationofANNhadnotbeenpopular… 相似文献
10.
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
11.
Matthew Browne Bruno Castelle Darrell Strauss Rodger Tomlinson Michael Blumenstein Chris Lane 《Coastal Engineering》2007
Estimation of swell conditions in coastal regions is important for a variety of public, government, and research applications. Driving a model of the near-shore wave transformation from an offshore global swell model such as NOAA WaveWatch3 is an economical means to arrive at swell size estimates at particular locations of interest. Recently, some work (e.g. Browne et al. [Browne, M., Strauss, D., Castelle, B., Blumenstein, M., Tomlinson, R., 2006. Local swell estimation and prediction from a global wind-wave model. IEEE Geoscience and Remote Sensing Letters 3 (4), 462–466.]) has examined an artificial neural network (ANN) based, empirical approach to wave estimation. Here, we provide a comprehensive evaluation of two data driven approaches to estimating waves near-shore (linear and ANN), and also contrast these with a more traditional spectral wave simulation model (SWAN). Performance was assessed on data gathered from a total of 17 near-shore locations, with heterogenous geography and bathymetry, around the continent of Australia over a 7 month period. It was found that the ANNs out-performed SWAN and the non-linear architecture consistently out-performed the linear method. Variability in performance and differential performance with regard to geographical location could largely be explained in terms of the underlying complexity of the local wave transformation. 相似文献
12.
背景误差相关结构的确定是影响海浪同化效果的关键因素之一。集合Kalman滤波是一种较为成熟的同化方法,其可以对背景误差进行实时更新和动态估计,现已广泛应用于海洋和大气领域的研究。本文基于MASNUM-WAM海浪模式,分别采用静态样本集合Kalman滤波和EAKF方法,针对2014年全球海域开展海浪数据同化实验,同化资料为Jason-2卫星高度计数据,利用Saral卫星高度计资料对同化实验结果进行检验。结果表明,两组同化方案均有效提高了海浪模式的模拟水平,EAKF方案在风场变化较大的西风带区域表现显著优于静态样本集合Kalman滤波方案,但总体上两者相差不大。综合考虑计算成本和同化效果,静态样本集合Kalman滤波方案更适用于海浪业务化预报。 相似文献
13.
V. G. Polnikov V. I. Dymov T. A. Pasechnik I. V. Lavrenov Z. K. Abuzyarov S. A. Sannasiraj 《Oceanology》2008,48(1):7-14
With the purpose of revealing the actual advantages of the new source function that was earlier proposed in [5] for use in numerical wind wave models, its testing and verification was carried out by means of modification of the WAM (Cycle-4) model. The verification was performed on the basis of a comparison of the results of wave simulation for a given wind field with the buoy observation data obtained in three oceanic regions. In the Barents Sea, this kind of comparison was made for wave observations from a single buoy with an interval of 6 hours for a period of 3 years. In two regions of the North Atlantic, the comparison was performed for 3 buoys in both regions for observation periods of 30 days with an interval of 1 hour. Estimations of the simulation accuracy were obtained for a series of wind wave parameters, and they were compared with the original and modified WAM model. Advantages of the modified model consisting of the enhancement of the calculation speed by 20–25% and a 1.5- to 2-fold increase in the simulation accuracy for the significant wave height and the mean period were proved. 相似文献
14.
The performance of an oscillating water column (OWC) wave energy converter depends on many factors, such as the wave conditions, the tidal level and the coupling between the chamber and the air turbine. So far most studies have focused on either the chamber or the turbine, and in some cases the influence of the tidal level has not been dealt with properly. In this work a novel approach is presented that takes into account all these factors. Its objective is to develop a virtual laboratory which enables to determine the pneumatic efficiency of a given OWC working under specific conditions of incident waves (wave height and period), tidal level and turbine damping. The pneumatic efficiency, or efficiency of the OWC chamber, is quantified by means of the capture factor, i.e. the ratio between the absorbed pneumatic power and the available wave energy. The approach is based on artificial intelligence—in particular, artificial neural networks (ANNs). The neural network architecture is chosen through a comparative study involving 18 options. The ANN model is trained and, eventually, validated based on an extensive campaign of physical model tests carried out under different wave conditions, tidal levels and values of the damping coefficient, representing turbines of different specifications. The results show excellent agreement between the ANN model and the experimental campaign. In conclusion, the new model constitutes a virtual laboratory that enables to determine the capture factor of an OWC under given wave conditions, tidal levels and values of turbine damping, at a lower cost and in less time than would be required for conventional laboratory tests. 相似文献
15.
The present study focused on tracing tsunami-drifted objects under a real tsunami based on an integrated numerical method. Instead of a solitary wave that is much shorter and steeper than real-world tsunami waves, an extra-long tsunami wave is represented here in a nearshore region using a new approach. To this end, propagation of a seismic tsunami from the source to the nearshore region was simulated using two-dimensional depth-averaged equations. When the waves reached the target coastal area, the time series of the free surface of the tsunami was approximated by a theoretical relation based on a combination of several solitons, which were then used to solve the linearized trajectory equation of the wave-maker to generate the intended time series of the tsunami wave. Finally, in a nearshore model, the movement of drifted bodies under the generated tsunami wave was simulated based on the smoothed-particle hydrodynamics (SPH) method. In order to verify the accuracy of the proposed method in tracing the drifted bodies under a real tsunami, the giant fish-oil tank, which was transported about 300 m during the 2011 Tohoku tsunami of Japan, was selected as the benchmark. The results demonstrate that the time series of the long tsunami wave was successfully generated by the piston wave-maker in the GPU-based SPH model, and the proposed approach can be regarded as a suitable alternative for reproduction of a real tsunami. The results also showed that the simulated fish-oil tank properly followed the estimated trajectory in Ishinomaki but it was transported more than the reported distance, which was expected due to absence of a holding connection between the tank and the ground in the SPH model. It should be emphasized that this study is one of the first studies on three-dimensional tracing of a tsunami-drifted body during a real event, and the tracing can be more accurate in further simulations by applying higher-resolution topography data and faster computation systems that help include more details in the nearshore model. 相似文献
16.
基于选定风浪方向谱的海浪模拟方法(英文) 总被引：1，自引：0，他引：1
简要回顾当前第三代海浪模式中的困难。为避开这些困难,作者提出一种新的海浪模拟方法,其中特定定义的风浪组成波依常风下随时间成长的方向谱计算,而涌浪组成波藉考虑涡动黏性和底摩擦加以计算。并进行了常风场和变风场下系统的数值试验。在常风速情形中,模拟结果能精确地化为建立模拟所根据的谱和风浪成长关系。计算显示出台风中心附近浪场的极端复杂的谱结构。当风速骤然降低时,模拟的波高减小与观测符合。在风向逐渐或骤然改变情形下,计算的时间响应尺度与海上观测符合,而且演化中的二维谱结构得到良好刻画。对于涌浪在无风下的传播,模拟结果合理,包括波参量及谱结构的变化。后报得到的波高、周期和海上资料符合。与第三代模式相比,文中提出的方法较易改进,需用的计算机时间显著减少。最后讨论采用一个已知谱来建立谱形式的海浪预报模型的合理性以及有关的问题。 相似文献
17.
The presently studied numerical model, e.g., composite roughness, is successful for the purpose of seafloor classification employing processed multibeam angular backscatter data from manganese-nodule-bearing locations of the Central Indian Ocean Basin. Hybrid artificial neural network (ANN) architecture, comprised of the self-organizing feature map and learning vector quantization (LVQ), has been implemented as an alternative technique for sea-floor roughness classification, giving comparative results with the aforesaid numerical model for processed multibeam angular backscatter data. However, the composite-roughness model approach is protracted due to the inherent need for processed data including system-gain corrections. In order to establish that tedious processing of raw backscatter values is unessential for efficient classification, hybrid ANN architecture has been attempted here due to its nonparametric approach. In this technical communication, successful employment of LVQ algorithm for unprocessed (raw) multibeam backscatter data indicates true real-time classification application. 相似文献
18.
Zhang Changkuan Wang Zhen Zhang Dongsheng
Associate Professor Dept. of Navigation Ocean Engineering Hohai University Nanjing
Lecturer Dept. of Navigation Ocean Engineering Hohai University Nanjing
Professor Dept. of Navigation Ocean Engineering Hohai University Nanjing 《中国海洋工程》1995,(2)
A numerical model of shoreline change of sand beaches based on long-term field wave data is proposed, the explicit and implicit finite difference forms of the model are described, and an application of the model is presented. Results of the application indicate that the model is sensitive to the order of the input wave data, and that the effects of long-term wave series and the effects of the mean annual wave conditions on the model are different. Instead of a single wave condition, the wave series will make the calibration and the verification of the model more practical and the results of the model more reasonable. 相似文献
19.
《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
20.
Large-scale wave reanalysis databases (0.1°–1° spatial resolution) provide valuable information for wave climate research and ocean applications which require long-term time series (> 20 years) of hourly sea state parameters. However, coastal studies need a more detailed spatial resolution (50–500 m) including wave transformation processes in shallow waters. This specific problem, called downscaling, is usually solved applying a dynamical approach by means of numerical wave propagation models requiring a high computational time effort. Besides, the use of atmospheric reanalysis and wave generation and propagation numerical models introduce some uncertainties and errors that must be dealt with. In this work, we present a global framework to downscale wave reanalysis to coastal areas, taking into account the correction of open sea significant wave height (directional calibration) and drastically reducing the CPU time effort (about 1000 ×) by using a hybrid methodology which combines numerical models (dynamical downscaling) and mathematical tools (statistical downscaling). The spatial wave variability along the boundaries of the propagation domain and the simultaneous wind fields are taking into account in the numerical propagations to performance similarly to the dynamical downscaling approach. The principal component analysis is applied to the model forcings to reduce the data dimension simplifying the selection of a subset of numerical simulations and the definition of the wave transfer function which incorporates the dependency of the wave spatial variability and the non-uniform wind forcings. The methodology has been tested in a case study on the northern coast of Spain and validated using shallow water buoys, confirming a good reproduction of the hourly time series structure and the different statistical parameters. 相似文献