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基于调和分析法与ANFIS系统的综合潮汐预报模型 总被引：1，自引：1，他引：0
港口沿岸地区以及河流入海口等地区的精确潮汐预报对于各种海洋工程作业有着非常重要的意义。潮汐水位的变化受到众多复杂因素的影响,而且这些复杂的因素往往有着较强的实变性和非线性。为了进一步提高沿岸港口码头等水域的潮汐水位的预测精度,本文提出了一种基于调和分析模型与自适应神经模糊推理系统相结合的模块化潮汐水位预测模型;并采用相关分析确定整个预测模型的输入维数;模块化将潮汐分解为两部分：由天体引潮力形成的天文潮部分和由各种天气以及环境因素引起非天文潮部分。其中调和分析法用于天文潮部分的预测,ANFIS用于预测具有较强非线性的非文潮部分。模块化综合了两种方法的优势,即调和分析法能够实现长期、稳定的天文潮预报,ANFIS能够以较高的精度实现潮汐非线性拟合与预测。模型使用ANFIS模型和调和分析模型分别对潮汐的非天文潮和天文潮部分进行仿真预测,然后将两部分的预测结果综合形成最终的潮汐预测值。此外,本文选用三种不同的模糊规则生成方法（grid partition （GP）,fuzzy c-means （FCM） and sub-clustering （SC））生成完整的ANFIS系统,并使用实测数据进行验证用以选取最优的ANFIS预测模型。最后将最优的ANFIS模型与调和分析模型相结合进行潮汐水位的最终预报。仿真实验选用Fort Pulaski潮汐观测站的实测潮汐值数据进行预报的仿真实验,仿真结果验证了该模型的可行性与有效性并取得了良好的效果,具有较高的预报精度。 相似文献
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海平面变化包含多种不同时间尺度信息，传统的预测方法仅对海平面变化趋势项、周期项进行拟合，难以利用海平面变化的不同时间尺度信号，使得预测精度不高。本文基于深度学习的预测模型，提出一种融合小波变换（wavelet transform，WT）与LSTM （long short-term memory，LSTM）神经网络的海平面异常组合预测模型。首先利用小波分解得到反映海平面变化总体趋势的低频分量和刻画主要细节信息的高频分量；然后通过LSTM神经网络对代表不同时间尺度的各个分量预测和重构，实现海平面变化的非线性预测。基于该模型的海平面变化预测的均方根误差、平均绝对误差和相关系数分别为12.76 mm、9.94 mm和0.937，预测精度均优于LSTM和EEMD-LSTM预测模型，WT-LSTM组合模型对区域海平面变化预测具有较好的应用价值。 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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《海洋技术学报》2023,(2)
为研究全球平均海平面与ENSO（El Niño-Southern Oscillation） 的相关性问题, 本文提出了一种结合局部均值分解和交叉小波原理的分析方法, 揭示全球平均海平面和ENSO 的影响机理和因果关联。利用全球平均海平面的时间序列进行局部均值分解得到PF 分量和余量, 表示海平面变化的高频分量、低频分量和趋势分量。剔除高频分量的影响, 利用最小二乘线性拟合趋势分量, 得到1991—2000 年的全球平均海平面上升速率为3.6 mm/a。接着对PF 的低频分量进行距平变换再与ONI 指数（Oceanic Niño Index, ONI） 分别进行Morlet 连续小波变换得到小波功率谱, 再将变换的连续小波分别进行交叉小波变换得到交叉小波功率谱和凝聚谱, 通过交
叉小波功率谱和交叉小波凝聚谱揭示信号在时频空间的能量共振和协方差分布规律, 其中交叉小波功率谱体现了共同的高能量区的相关性, 交叉小波凝聚谱体现了共同的低能量区的相关性。结果表明, 该方法能在多尺度上分析海平面的变化, 并能分析ONI 指数与全球平均海平面的关系, 可为全球平均海平面演变规律分析和预测等方面提供有力工具。 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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This article adopts Multivariate Adaptive Regression Spline (MARS) for prediction of Angle of Shearing Resistance(?) of soil. MARS is an adaptive, non-parametric regression approach. Percentages of fine-grained (FG), coarse-grained (CG), liquid limit (LL), and bulk density (BD) have been used as input variables of MARS. The developed MARS gives an equation for prediction of ? of soil. The results of MARS have been compared with Genetic Expression Programming (GEP), Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) models. These results demonstrate that the developed MARS can be used as a robust model for determination of ? of soil. 相似文献
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Iman Malekmohamadi Mohammad Reza Bazargan-Lari Reza Kerachian Mohammad Reza Nikoo Mahsa Fallahnia 《Ocean Engineering》2011,38(2-3):487-497
Wave Height (WH) is one of the most important factors in design and operation of maritime projects. Different methods such as semi-empirical, numerical and soft computing-based approaches have been developed for WH forecasting. The soft computing-based methods have the ability to approximate nonlinear wind–wave and wave–wave interactions without a prior knowledge about them. In the present study, several soft computing-based models, namely Support Vector Machines (SVMs), Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used for mapping wind data to wave height. The data set used for training and testing the simulation models comprises the WH and wind data gathered by National Data Buoy Center (NDBC) in Lake Superior, USA. Several statistical indices are used to evaluate the efficacy of the aforementioned methods. The results show that the ANN, ANFIS and SVM can provide acceptable predictions for wave heights, while the BNs results are unreliable. 相似文献
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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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Accurate prediction of longshore sediment transport in the nearshore zone is essential for control of shoreline erosion and beach evolution. In this paper, a hybrid Adaptive-Network-Based Fuzzy Inference System (ANFIS), Fuzzy Inference System (FIS), CERC, Walton–Bruno (WB) and Van Rijn (VR) formulae are used to predict and model longshore sediment transport in the surf zone. The architecture of ANFIS consisted of three inputs (breaking wave height), (breaking angle), (wave period) and one output (longshore sediment transport rate). For statistical comparison of predicted and measured sediment transport, bias, root mean square error and scatter index are used. The longshore sediment transport rate (LSTR) and wave characteristics at a 4 km-long beach on the central west coast of India are used as case studies. The CERC, WB and VR methods are also applied to the same data. Results indicate that the errors of the ANFIS model in predicting wave parameters are less than those of the empirical formulas. The scatter index of the CERC, WB and VR methods in predicting LSTR is 51.9%, 27.9% and 22.5%, respectively, while the scatter index of the ANFIS model in the prediction of LSTR is 17.32%. A comparison of results reveals that the ANFIS model provides higher accuracy and reliability for LSTR estimation than the other techniques. 相似文献
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Genetic programming (GP) has nowadays attracted the attention of researchers in the prediction of hydraulic data. This study presents Linear Genetic Programming (LGP), which is an extension to GP, as an alternative tool in the prediction of scour depth below a pipeline. The data sets of laboratory measurements were collected from published literature and were used to develop LGP models. The proposed LGP models were compared with adaptive neuro-fuzzy inference system (ANFIS) model results. The predictions of LGP were observed to be in good agreement with measured data, and quite better than ANFIS and regression-based equation of scour depth at submerged pipeline. 相似文献
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Genetic programming (GP) has nowadays attracted the attention of researchers in the prediction of hydraulic data. This study presents linear genetic programming (LGP), which is an extension to GP, as an alternative tool in the prediction of scour depth around a circular pile due to waves in medium dense silt and sand bed. Field measurements were used to develop LGP models. The proposed LGP models were compared with adaptive neuro-fuzzy inference system (ANFIS) model results. The predictions of LGP models were observed to be in good agreement with measured data, and quite better than ANFIS and regression-based equation of scour depth at circular piles. The results were tabulated in terms of statistical error measures and illustrated via scatter plots. 相似文献
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Learning from data for wind-wave forecasting 总被引：1，自引：0，他引：1
Along with existing numerical process models describing the wind-wave interaction, the relatively recent development in the area of machine learning make the so-called data-driven models more and more popular. This paper presents a number of data-driven models for wind-wave process at the Caspian Sea. The problem associated with these models is to forecast significant wave heights for several hours ahead using buoy measurements. Models are based on artificial neural network (ANN) and instance-based learning (IBL) .To capture the wind-wave relationship at measurement sites, these models use the existing past time data describing the phenomenon in question. Three feed-forward ANN models have been built for time horizon of 1, 3 and 6 h with different inputs. The relevant inputs are selected by analyzing the average mutual information (AMI). The inputs consist of priori knowledge of wind and significant wave height. The other six models are based on IBL method for the same forecast horizons. Weighted k-nearest neighbors (k-NN) and locally weighted regression (LWR) with Gaussian kernel were used. In IBL-based models, forecast is made directly by combining instances from the training data that are close (in the input space) to the new incoming input vector. These methods are applied to two sets of data at the Caspian Sea. Experiments show that the ANNs yield slightly better agreement with the measured data than IBL. ANNs can also predict extreme wave conditions better than the other existing methods. 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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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