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Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea 总被引：1，自引：0，他引：1
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. 相似文献
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Bang-Fuh CHEN 《中国海洋工程》2007,21(4):659-675
In multiresolution analysis(MRA)by wavelet function Daubechies(db),we decompose the signal to two parts,the low and high frequency content.The high-frequency content of the data is removed first and a new "de-noise" signal is reconstructed by using inverse wavelet transform.The wavelet spectrum and harmonic analysis were used to analyze the characteristics of tidal data before constructing the input and output structure of ANN model.That is,the concept of tidal constituent phase-lags was introduced and the new "de-noise" signal was used as the input data set of ANN and the forecasting accuracy of ANN model is significantly improved. 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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Back-propagation neural network for long-term tidal predictions 总被引：5，自引：0，他引：5
During the recent years, the availability of accurate ocean tide models has become increasingly important, as tides are the main contributor to disposal and movement of sediments, tracers and pollutants, and to a whole range of offshore applications in engineering, environmental observations, exploration and oceanography. Tides can be conventionally predicted by harmonic analysis, which is the superposition of many sinusoidal constituents with amplitudes and frequencies determined by a local analysis of the measured tide. However, accurate predictions of tide levels could not be obtained without a large number of tide measurements by the harmonic method. An application of the back-propagation neural network using short-term measuring data is presented in this paper. On site tidal level data at Taichung Harbor in Taiwan will be used to test the performance of the present model. Comparisons with conventional harmonic methods indicate that the back-propagation neural network mode also efficiently predicts the long-term tidal levels. 相似文献
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基于潮汐表数据同化的天文潮数值预报模型及其模拟预报效果 总被引：1，自引：0，他引：1
潮汐表是利用长期潮汐观测结果经调和分析实现的主要港湾潮汐预报结果,具有较高的预报精度,而通常的天文潮数值预报目前还难以达到潮汐表的预报精度.本研究在建立常规天文潮数值预报模型的基础上,建立了基于潮汐表数据同化的天文潮数值预报模型,并分别采用这2种模型预报福建沿岸海域的天文潮.其结果表明同化模型的预报结果无论是在潮时还是在潮高均明显优于常规模型;同化模型能显著地改善所研究的沿岸海域90个水位点中至少45个水位点的潮汐预报结果,而其他水位点的预报结果也有不同程度地改善. 相似文献
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This study provides a practical guide to the use of classical tidal prediction algorithms in coastal numerical forecasting models such as tide and tide-storm-surge models. Understanding tidal prediction parameter formulas and their limitations is key to successfully modifying and upgrading tidal prediction modules in order to increase the accuracy of perpetual interannual simulations and, in particular, storm-surge modeling studies for tide-dominated coastal environments. The algorithms for the fundamental prediction parameters, the five astronomical variables, used in tidal prediction are collated and tested. Comparisons between their estimation using different parameterizations shows that these methods yield essentially the same results for the period 1900–2099, revealing all are applicable for tidal forecasting simulation. Through experiments using a numerical model and a harmonic prediction program, the effects of nodal modulation correction and its update period on prediction accuracy and sensitivity are examined and discussed using a case study of the tidally-dominated coastal regime off the west coast of Korea. Results indicate that this correction needs updating within <30 days for accurate perpetual interannual tidal and mean sea-level predictions, and storm-surge model predictions requiring centimeter accuracy, for tidally-dominated coastal regimes. Otherwise, unacceptable systematic errors occur. 相似文献
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Study of tide prediction method influenced by nonperiodic factors based on support vector machines 总被引：1，自引：1，他引：0
Harmonic analysis, the traditional tidal forecasting method, cannot take into account the impact of noncyclical factors, and is also based on the BP neural network tidal prediction model which is easily limited by the amount of data. According to the movement of celestial bodies, and considering the insufficient tidal characteristics of historical data which are impacted by the nonperiodic weather, a tidal prediction method is designed based on support vector machine (SVM) to carry out the simulation experiment by using tidal data from Xiamen Tide Gauge, Luchaogang Tide Gauge and Weifang Tide Gauge individually. And the results show that the model satisfactorily carries out the tide prediction which is influenced by noncyclical factors. At the same time, it also proves that the proposed prediction method, which when compared with harmonic analysis method and the BP neural network method, has faster modeling speed, higher prediction precision and stronger generalization ability. 相似文献
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根据渤海10个站和大连、烟台共12个站同步一年的潮汐资料,采用调和分析和响应分析方法,分离出逐时的增减水.计算表明,对大多数海港,依据两种方法得到的增减水量值是一致的,对于非线性效应明显的港口,两者的结果相差较大.同时,还分析了渤海几次较强增减水过程的变化规律. 相似文献
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河口潮汐过程受上游径流、外海潮波等综合因素影响,动力机制复杂,潮位预报难度大。本文提出了一种基于非稳态调和分析（NS_TIDE）和长短时记忆（LSTM）神经网络的混合模型,对河口潮位进行12~48 h短期预报。该模型首先对河口实测潮汐数据进行非稳态调和分析,通过与实测资料对比得到分析误差的时序序列;以此作为LSTM神经网络的输入数据,通过网络学习并预测未来12~48 h潮位预报误差,据此对NS_TIDE的预测结果进行实时校正。利用该模型对2020年长江口潮位过程进行了预报检验,结果表明混合模型12 h、24 h、36 h和48 h短期水位预报的均方根误差（RMSE）相比NS_TIDE模型至多分别降低了0.16 m、0.15 m、0.14 m和0.12 m;针对2020年南京站最高水位预测,NS_TIDE模型预报误差为0.64 m,而混合模型预报误差仅为0.10 m。 相似文献
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Accurate prediction of tidal level including strong meteorologic effects is very important for human activities in oceanic and coastal areas. The contribution of non-astronomical components to tidal level may be as significant as that of astronomical components under the weather, such as typhoon and storm surge. The traditional harmonic analysis method and other models based on the analysis of astronomical components do not work well in these situations. This paper describes the Back-Propagation Neural Network (BPNN) approach, and proposes a method of iterative multi-step prediction and the concept of periodical analysis. The prediction among stations shows that the BPNN model can predict the tidal level with great precision regardless of different tide types in different regions. Based on the non-stationary characteristic of hourly tidal record including strong meteorologic effects, three Back-Propagation Neural Network models were developed in order to improve the accuracy of prediction and supplement of tidal records: (1) Difference Neural Network model (DNN) for the supplementing of tidal record; (2) Minus-Mean-Value Neural Network model (MMVNN) for the corresponding prediction between tidal gauge stations; (3) Weather-Data-based Neural Networks model (WDNN) for set up and set down.The results show that the above models perform well in the prediction of tidal level or supplement of tidal record including strong meteorologic effects. 相似文献
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感潮水闸流量的准确计算对于河网地区水闸引排水效益的分析和水闸综合管理体系的建立具有非常重要的意义。对感潮水闸的水力特性进行了详细分析,认为感潮水闸具有瞬时性和非线性等水力特点,提出采用人工神经网络理论建立其过流量的计算模型。建立了三种计算模式,应用浦东新区东沟水闸资料对不同模式进行了训练、测试和比较,推荐以水闸内外河水位、闸门开启度和上一时刻流量作为神经网络输入的计算模型为最终感潮水闸流量计算模型。研究表明,人工神经网络可以较好地隐含识别感潮水闸的多种出流类型,并具有较强的泛化和容错能力,从而为感潮河网地区水闸流量的计算提供了一种新的解决途径。 相似文献
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台风的风暴潮是台风引发的一种重要次生灾害，对沿海城市带来的威胁是多方面的。及时准确地预报风暴潮，对沿海地区采取合理措施减少人员伤亡和经济损失具有重要意义。本文利用长短期记忆神经网络 （LSTM） 模型，综合考虑风速、
风向、气压等气象因素和前时序的潮位数据，建立了风暴潮的临近预报模型。结果表明，基于 LSTM 的临近预报模型具有相当的预报技巧，利用前时序的风速和风向数据以及潮位数据建立的模型可对风暴潮潮位进行准确地预测。研究还表明，仅考虑前时序潮位的预测模型误差最大，考虑气压后的模型预测能力有一定进步，而考虑风的要素以后，预测的效果提升更为明显。 相似文献
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潮位预测严重影响沿海区域，尤其是近海浅水沿岸地区居民的生产生活和涉海活动。谐波分析是长周期潮位预测的传统方法，但无法预测非周期性气象过程发生时的水位变化。与数据处理方法相结合，人工智能的方法通过拟合输入与输出数据的历史数值关系，能够有效预测高度非线性和非平稳的流模式，因而在时间序列数据预测领域得到了广泛的应用。本文结合自适应模糊推理系统（Adaptive Neuro-Fuzzy Inference System, ANFIS）和小波分解方法，利用水位异常和风切变分量作为输入数据，实现了一种综合的多时效潮位预测方法。文中测试了多种输入变量组合和小波-ANFIS（WANFIS）模型，并与人工神经网络（Artificial Neural Network, ANN）、小波-ANN（WANN）和ANFIS模型进行了预测结果对比。通过不同指数的误差分析来看，相比ANN模型，ANFIS模型能够更准确的预测潮位变化，小波分解对ANFIS预测精度有一定的提高，且模型中水位异常和风切变分量数据的加入比单一的潮位数据输入能取得更好的预测结果。 相似文献
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广东沿海台风风暴潮可视化预报系统 总被引：5，自引：0，他引：5
广东省地处南海北部,风暴潮灾害严重。为快速准确做好风暴潮预报并将预报结果应用于防灾减灾中,根据南海预报中心多年来在风暴潮数值预报、经验统计方法预报和潮汐预报的实践,研制了可视化软件。此软件可显示广东省28个沿海主要港口的逐时风暴增水与天文潮位的综合潮位曲线与数值,以动态或静态显示广东沿海海面的增水等值线图,成为业务化预报软件。多年的风暴潮数值预报的实践证明,国家海洋环境预报中心王喜年等在八·五攻关项目中推广应用的台风风暴潮模式,在广东沿岸的风暴潮数值预报中效果较好,可视化预报软件采用这一模式是合适的。 相似文献