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1.
Tayeb Sadeghifar Maryam Nouri Motlagh Massoud Torabi Azad Mahdi Mohammad Mahdizadeh 《Marine Geodesy》2017,40(6):454-465
The prediction of wave parameters has a great significance in the coastal and offshore engineering. For this purpose, several models and approaches have been proposed to predict wave parameters, such as empirical, soft computing, and numerical based approaches. Recently, soft computing techniques such as recurrent neural networks (RNN) have been used to develop sea wave prediction models. In this study, the RNN for wave prediction based on the data gathered and the measurement of the sea waves in the Caspian Sea, in the north of Iran is used for this study. The efficiency of RNNs for 3, 6, and 12 hourly and diurnal wave prediction using correlation coefficients is calculated to be 0.96, 0.90, 0.87, and 0.73, respectively. This indicates that wave prediction by using RNNs yields better results than the previous neural network approaches. 相似文献
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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
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Owing to the spatial averaging involved in satellite sensing, use of observations so collected is often restricted to offshore regions. This paper discusses a technique to obtain significant wave heights at a specified coastal site from their values gathered by a satellite at deeper offshore locations. The technique is based on the approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) type. The satellite-sensed data of significant wave height; average wave period and the wind speed were given as input to the network in order to obtain significant wave heights at a coastal site situated along the west coast of India. Qualitative as well as quantitative comparison of the network output with target observations showed usefulness of the selected networks in such an application vis-à-vis simpler techniques like statistical regression. The basic FFBP network predicted the higher waves more correctly although such a network was less attractive from the point of overall accuracy. Unlike satellite observations collection of buoy data is costly and hence, it is generally resorted to fewer locations and for a smaller period of time. As shown in this study the network can be trained with samples of buoy data and can be further used for routine wave forecasting at coastal locations based on more permanent flow of satellite observations. 相似文献
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董昌明 LIM KAM SIAN Kenny Thiam Choy 蒋星亮 曹玉晗 嵇宇翔 王森 余洋 陆晓婕 周书逸 韦销蔚 BETHEL Brandon Justin 徐广珺 董济海 孙文金 王海丽 单海霞 王锦 王东霞 滕芳园 曹茜 谢文鸿 游志伟 王子韵 林连杰 《海洋科学进展》2022,40(4):660-683
海洋预报是进行海上活动的安全保障,海洋预报系统技术已经成为现代海洋气象业务的技术支撑。海洋观测、数据同化、数值模拟和高性能计算机等技术的进步极大地推动着海洋业务化预报的发展。采用大气数值模式（WRF）、海洋数值模式（CROCO）和海浪数值模式（SWAN）的多模式高分辨率离线耦合方式,添加南京信息工程大学“海洋数值模拟与观测实验室”团队自主研发的一系列海洋模式参数化方案,包括浪致混合参数化方案、亚中尺度参数化方案、海山诱导混合参数化方案以及涡旋诱导的沿等密度面和跨等密度面混合参数化方案,并通过同化技术和最新的人工智能技术与观测资料相结合,构建一种面向中国边缘海的风浪流多参数耦合预报系统,用于海上风电功率的预报和其他海洋灾害预警。实际观测资料的验证表明,该预报系统能较准确地模拟海上风场、海流、海温、波浪、潮汐等海洋气象要素。同时实现了按需实时可视化全景展示。 相似文献
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To explore new operational forecasting methods of waves, a forecasting model for wave heights at three stations in the Bohai Sea has been developed. This model is based on long short-term memory(LSTM) neural network with sea surface wind and wave heights as training samples. The prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input, the prediction error produced by the proposed LSTM model at Sta. N01 is 20%, 18% and 23% lower than the conventional numerical wave models in terms of the total root mean square error(RMSE), scatter index(SI) and mean absolute error(MAE), respectively. Particularly, for significant wave height in the range of 3–5 m, the prediction accuracy of the LSTM model is improved the most remarkably, with RMSE, SI and MAE all decreasing by 24%. It is also evident that the numbers of hidden neurons, the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy. However, the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used. The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training. Overall, long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting. 相似文献
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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
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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
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深海极端波浪环境为浮式海洋平台作业时最为关键的海洋动力环境之一。在其作用下,深海浮式平台的运动、气隙以及结构响应等均为近年来的研究热点。然而,在深海环境中,入射波浪环境往往通过X波段雷达进行测量,仅能获得波浪的短时统计值,极大限制了实海域浮动平台动力响应的研究。目前,尚无成熟的方法能够对海洋浮式平台所处海域的入射波时序进行实时测量。针对深远海半潜式平台的波浪时序随船测量问题,结合平台气隙响应与运动响应数据建立基于深层神经网络的波浪非线性解耦模型,准确估计辐射、绕射波浪以及其非线性成分对时序波浪场的影响。研究显示,基于深度神经网络的波浪时序测量技术可以实现从气隙响应到入射波信息的反推,利用该方法计算得到的波浪时序具有较高的精度。 相似文献
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Choi Young-Kwang Seo Seung-Nam Choi Jin-Yong Shi Fengyan Park Kwang-Soon 《海洋学报(英文版)》2019,38(7):36-47
A wave forecasting system using FUNWAVE-TVD which is based on the fully nonlinear Boussinesq equations by Chen(2006) was developed to provide an accurate wave prediction in the Port of Busan, South Korea. This system is linked to the Korea Operational Oceanographic System(KOOS) developed by Park et al.(2015). The computational domain covers a region of 9.6 km×7.0 km with a grid size of 2 m in both directions, which is sufficient to resolve short waves and dominant sea states. The total number of grid points exceeds 16 millions,making the model computational expensive. To provide real-time forecasting, an interpolation method, which is based on pre-calculated results of FUNWAVE-TVD and SWAN forecasting results at the FUNWAVE-TVD offshore boundary, was used. A total of 45 cases were pre-calculated, which took 71 days on 924 computational cores of a Linux cluster system. Wind wave generation and propagation from the deep water were computed using the SWAN in KOOS. SWAN results provided a boundary condition for the FUNWAVE-TVD forecasting system. To verify the model, wave observations were conducted at three locations inside the port in a time period of more than 7 months. A model/model comparison between FUNWAVE-TVD and SWAN was also carried out. It is found that, FUNWAVE-TVD improves the forecasting results significantly compared to SWAN which underestimates wave heights in sheltered areas due to incorrect physical mechanism of wave diffraction, as well as large wave heights caused by wave reflections inside the port. 相似文献
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海浪直接影响海上活动和航行安全,同时也蕴藏着巨大的可再生能源,对海浪核心参数之一波高预测至关重要。基于2015年7月～2022年6月山东小麦岛(36°N,120.6°E)站点实测的波高数据,利用反向传播神经网络(back-propagation neural network,BPNN)、长短记忆网络(long short-term memory, LSTM)和支持向量机回归(support vector regression, SVR)三种机器学习模型对波高进行预测,并分析了瑞利参数的引入对预测结果的影响。结果显示,模型输入项引入瑞利参数后,对1 h和6 h波高预测提升效果有限,预测值与测试集的相关性提升不超过0.02,均方根误差的降低不超过0.01 m;在12h和24h的预测中,BPNN和LSTM模型预测结果相关性提升0.03～0.07,均方根误差降低0.02～0.03m,而SVR模型预测结果变化不显著。说明瑞利参数有助改善BPNN和LSTM模型中长期海浪预报。此外,特征扰动方法(机器学习中特征重要性的计算方法之一)验证了瑞利参数在波高预测中的重要性,瑞利参数的引入为波高的机器学习预... 相似文献
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文章构建国内外首份"21世纪海上丝绸之路"波浪能资源大数据框架,主要包括波浪能气候背景特征、波浪能等级区划、波浪能短期预报、涌浪能特征、资源长期变化趋势和资源中长期预测6大模块;按照三维网格和时间序列实现波浪能信息的全面数字化和全息化存储,对数据进行质量控制并实现四维可视化。研究成果可广泛运用于海上风能和海流能等海洋新能源的大数据建设,为国家和参与"21世纪海上丝绸之路"建设的决策人员、研究人员和工程人员提供数据支撑和决策支持,助力"21世纪海上丝绸之路"新能源开发。 相似文献
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江苏近海地层原位剪切波速相关特性及预测方法研究 总被引：1，自引：0，他引：1
剪切波速测试是原位勘测常用且有效的技术之一,其测试成果可用于分析场地土层动力学特性。海洋地层测试条件恶劣,在某些情况下对剪切波速的预测分析尤为重要。为了研究海洋地层精确的剪切波速预测方法,结合江苏近海及潮间带的剪切波速原位测试成果,总结和对比分析了剪切波速预测方法,评判了剪切波速的变化特性和与土体物理力学指标的统计关系。基于广义回归神经网络(GRNN)方法,通过剪切波速与土体各参数的统计关系,建立了剪切波速与土体各物理力学指标的非线性映射关系,进行了剪切波速的预测分析,得到了较好的预测结果。 相似文献
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《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
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1 .IntroductionWiththedevelopmentofoceantechnology ,moreandmoreextremelylargeandlongflexibleoff shoreplatformsusedforoilexplorationanddrillingoperationarebuiltinhostileoceanenvironments .Ingeneral,thiskindofplatformsisanonlineardistributedparametersystemanditsnaturalfrequencyfallsclosertothedominantwavefrequencieswiththeincreaseofwaterdepth .Besides ,itsstructureisverycomplexandtheexternalwaveforceontheplatformisuncertain .Thus ,theseplatformsarepronetoexcessivewave inducedoscillationsunderbot… 相似文献
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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
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Due to uncertainties and random behavior of sea loads, presenting an accurate hydrodynamic analysis for motion control of offshore ships is a big challenge. This paper aims to propose a novel method for motion control of a crew transfer vessel (CTV) in order to ensure safe crew transfer to an offshore wind turbine (OWT). For this purpose, we propose a novel neural network observer-based optimal control (NOPC) scheme to tackle unknown dynamics and disturbances, nonlinear effects, and non-symmetric control input saturation constraints. Accordingly, the neural network (NN) structure addresses the Hamilton-Jacobi-Bellman (HJB) equation and forms an optimal control signal remaining in the saturation bounds. The Lyapunov theory guarantees the Ultimately Uniformly Boundedness (UUB) of all signals of the closed-loop system. The high performance of the presented method is demonstrated in regular waves with high frequency in comparison with the previous studies. It is worth mentioning that there are not any limitations to implement the adopted strategy for other offshore applications. 相似文献
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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