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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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Application of artificial neural networks in tide-forecasting 总被引：3，自引：0，他引：3
An accurate tidal forecast is an important task in determining constructions and human activities in ocean environments. Conventional tidal forecasting has been based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large number of parameters are required for the prediction of a long-term tidal level with harmonic analysis. Unlike conventional harmonic analysis, this paper presents an artificial neural network (ANN) model for forecasting the tidal-level using the short term measuring data. The ANN model can easily decide the unknown parameters by learning the input–output interrelation of the short-term tidal records. Three field data with three types of tides will be used to test the performance of the proposed ANN model. The numerical results indicate that the hourly tidal levels over a long duration can be predicted using a short-term hourly tidal record. 相似文献
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利用1921–2020年的海平面气压、海平面高度、热含量数据以及海冰密集度作为太平洋年代际振荡（Pacific Decadal Oscillation, PDO）指数的预报要素,建立了关于PDO指数时间序列预测的多变量长短期记忆（Long Short Term Memory, LSTM）神经网络模型,对比分析了2011–2020年不同时间序列预测模型的PDO指数预测结果,最后利用多变量LSTM神经网络模型实现了2021–2030年的PDO指数预测。结果显示,多变量LSTM神经网络模型的预测值与观测值经过交叉验证后的平均相关系数和均方根误差分别为0.70和0.62;PDO未来10年将一直处于冷位相,PDO神经网络指数出现两次波动,于2025年出现最小值。相比于其他时间序列预测模型,本文采用的多变量LSTM神经网络模型预测结果误差小、拟合效果好,可以作为一种新型的预测PDO指数的手段。 相似文献
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海面高度异常是反映海洋环境状况的主要变量之一。本文使用1993—2019年的融合月均海面高度异常数据,建立了基于深度学习的海面高度异常预测神经网络模型,提出了基于融合U型网络(U-Net)和卷积长短记忆网络(ConvLSTM)的中长期海面高度异常预报模型。在研究海域0.25°×0.25°的空间分辨率下,模型测试集预报结果的均方根误差和平均绝对误差分别为0.039 m和0.027 m,均优于全连接LSTM预报模型和ConvLSTM+CNN预报模型,为大中尺度的海面高度异常预报提供了新的方法。 相似文献
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采用三维浅海湖波定解方程组,建立番禺附近海域的三维潮流数值模型来计算潮流和潮位变化情况,开边界采用调和常数计算的水位来驱动,潮流和潮位验证结果较好,模拟结果较真实的反应了番禺附近海域的潮流和潮位情况.在潮流模拟验证正确的前提下,建立溢油预测数值模型,采用欧拉-拉格朗日追踪方法,对油膜中心轨迹进行预测,并预测出油膜的平均... 相似文献
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海冰管理是抵御寒区海洋资源开发海冰威胁的有效手段,海冰风险的准确、快速预测是海冰管理系统的关键组成部分。文中面向海冰管理中的冰情短时预测需求,明确了基于现场监测的海冰风险预测模式,开展了应用机械学习理论的海冰风险短时预测方法研究,并以渤海辽东湾海冰管理为例,讨论了神经网络与小波分解等非线性预测方法在冰情短时预测中的适用性。结果表明,时间序列小波神经网络在短时(6 h)冰厚预测中的预测精度与Elman神经网络相仿,而在24~48 h预测中的精度偏差较大;Elman神经网络在6 h、24 h与48 h的冰厚预测中均能保持较好的预测精度,在冰流速与来冰方向预测中,模型预测精度达到80%左右。 相似文献
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赤潮预测的人工神经网络方法初步研究 总被引：13，自引：0，他引：13
赤潮是一种由多因素综合作用引发的生态异常现象,具有突发性及非线性等特点。对其进行预测预报一直是海洋科学研究的热点。探讨了应用人工神经网络原理进行赤潮预测的方法,简要介绍了BP和RBF算法的基本原理,用2种算法对不同海域赤潮生物与环境因子之间非线性和不确定性的复杂关系进行学习训练和预测检验,并与传统的统计方法进行了比较。结果表明：人工神经网络方法在模拟和预测方面优于传统的统计回归模型,具有较强的模拟预测能力及实用性,值得进一步探索。 相似文献
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基于人工神经网络方法,利用海面水温、海面风速以及海面气压反演南海近海面气温,采用的基础数据集是国际综合海洋-大气数据集(International Comprehensive Ocean-Atmosphere Data Set,2.4 Release,ICOADS2.4)1981—2008年的观测资料,其中1981—2000年的观测资料用来建立模型,2001—2008年的观测资料用来进行模型检验。采用的人工神经网络方法是引入动量因子并采用批处理梯度下降法的BP(Back propagation)算法。试验结果表明,基于人工神经网络建立的近海面气温反演方法明显优于多元线性回归方法,尤其是在春季和冬季,海面水温、海面风速以及海面气压与近海面气温之间存在较强的非线性关系,人工神经网络的优势更加明显。总体而言,人工神经网络在各月的反演效果较均衡,均方根误差介于1.5—1.8℃之间,平均绝对误差为1.1—1.3℃。 相似文献
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The Jason-1 Mission 总被引：1，自引：2，他引：1
Yves M nard Lee-Lueng Fu P. Escudier F. Parisot J. Perbos P. Vincent S. Desai B. Haines G. Kunstmann 《Marine Geodesy》2003,26(3):131-146
On December 7, 2001, the Jason-1 satellite was successfully launched by a Boeing Delta II rocket from the Vandenberg site in California, USA. Its main mission was to maintain the high accuracy altimeter measurements, provided since 1992 by TOPEX/Poseidon (T/P), ensuring continuity in observing and monitoring the ocean for intraseasonal to interannual changes, mean sea level, tides, and so forth. Despite four times less mass and power, the Jason-1 system has been designed to have the same performances as T/P, measuring sea surface topography at the centimeter level. This new Centre National d'Etudes Spatiales/National Aeronautics and Space Administration (CNES/NASA) mission also provides near real-time data for sea state and ocean forecast. The first 10 months of the Jason mission were dedicated to the verification of the system performance and cross-calibration with T/P measurements. A complete CALVAL plan was conducted by the Science and Project Teams of the mission based on in situ and regional experiments, global statistical approaches, and multisatellite comparisons, taking advantage of the T/P-Jason overlap during the first months of the mission. CALVAL and first science results showed that the Jason-1 performances were compliant with prelaunch specifications. This was a needed preamble before starting the routine phase of the mission in July 2003 with generation and distribution of validated geophysical data records to the whole user community. 相似文献
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《Marine Geodesy》2013,36(3-4):131-146
On December 7, 2001, the Jason-1 satellite was successfully launched by a Boeing Delta II rocket from the Vandenberg site in California, USA. Its main mission was to maintain the high accuracy altimeter measurements, provided since 1992 by TOPEX/Poseidon (T/P), ensuring continuity in observing and monitoring the ocean for intraseasonal to interannual changes, mean sea level, tides, and so forth. Despite four times less mass and power, the Jason-1 system has been designed to have the same performances as T/P, measuring sea surface topography at the centimeter level. This new Centre National d'Etudes Spatiales/National Aeronautics and Space Administration (CNES/NASA) mission also provides near real-time data for sea state and ocean forecast. The first 10 months of the Jason mission were dedicated to the verification of the system performance and cross-calibration with T/P measurements. A complete CALVAL plan was conducted by the Science and Project Teams of the mission based on in situ and regional experiments, global statistical approaches, and multisatellite comparisons, taking advantage of the T/P-Jason overlap during the first months of the mission. CALVAL and first science results showed that the Jason-1 performances were compliant with prelaunch specifications. This was a needed preamble before starting the routine phase of the mission in July 2003 with generation and distribution of validated geophysical data records to the whole user community. 相似文献
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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
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采用2010—2017年南海5个浮标波高观测资料和中国气象局热带气旋最佳路径集中的热带气旋参数, 基于前馈型误差反向传播(Forward Feedback Back Propagation, FFBP)神经网络(Artificial Neural Network, ANN)方法, 分别建立了各浮标站的台风浪高快速计算模型。研究显示, 基于热带气旋中心坐标、中心最低气压、近中心最大风速、热带气旋中心与浮标之间的距离和方位4个参数建立的神经网络模型经反复训练后, 模型输出结果可以很好地拟合观测数据, 各浮标有效波高计算值与观测值的均方根误差小于0.3m, 平均相对误差为5.78%~7.23%, 相关系数大于0.9, 属高度相关。独立测试结果显示, “山竹”( 国际编号: 1822)影响期间有效波高最大值的神经网络模型预报结果与观测值基本吻合, 相对误差为-31.06%~0.98%, 但计算的最大值出现时间和观测情况不完全一致。该计算方法可应用于热带气旋影响期间的有效波高最大值计算, 因而在海洋工程领域和海洋预报领域具有应用前景。 相似文献
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《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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Numerical sea ice prediction in China 总被引：5，自引：2，他引：3
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基于无结构有限体积法海洋模式(FVCOM),建立了马尔代夫双重嵌套的水位、海流预报模式,并实现了业务化运行。利用三角网格提高重点区域(马尔代夫大桥及岛屿附近海域)的分辨率,最高网格分辨率达到45 m。垂向分层采用σ-s混合坐标的方式划分,分为31层,分别在表层和底层进行加密。采用GFS预报的风场、气压场和热通量结果制作模式表面强迫场文件。在开边界处与HYCOM预报结果进行嵌套,在斜压条件下,采用热启动的方式,业务化模拟了马尔代夫海域2020年的水位流场过程。结果表明,模式能够较好地再现计算海域内天文潮和综合水位的预报,模式预报的水位值与潮位站实测值非常接近。 相似文献
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The present study describes a novel way of a systematic and objective selection procedure for the development of an Artificial Neural Network-based storm Surge Forecast Model (ANN-SFM) with the 5, 12 and 24 h-lead times and its application to Sakai Minato area on the Tottori coast, Japan. The selection procedure guides how to determine the superiority of the best performing model in terms of the appropriate combination of unit number in the hidden layer and parameter in the input layer. In the application of ANN-SFM to Sakai Minato, it is found that the best 5 and 12 h-forecast ANN-SFMs are established with the most suitable set of 70 units (the number of hidden neurons) and the input components of surge level, sea level pressure, the depression rate of sea level pressure, longitude, latitude, central atmospheric pressure and highest wind speed. The best 24 h-forecast ANN-SFM is determined with 160 units and the input parameters of surge level, sea level pressure, the depression rate of sea level pressure, longitude and latitude. The proposed method of the selection procedure is able to be adaptable to other coastal locations for the development of the artificial neural network-based storm surge forecast model as establishing the superiority of the most relevant set combining unit numbers and input parameters. 相似文献