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1.
An accurate estimation of scour depth around piles is important for coastal and ocean engineers involved in the design of marine structures. Owing to the complexity of the problem, most conventional approaches are often unable to provide sufficiently accurate results. In this paper, an alternative attempt is made herein to develop adaptive neuro-fuzzy inference system (ANFIS) models for predicting scour depth as well as scour width for a group of piles supporting a pier. The ANFIS model provides the system identification and interpretability of the fuzzy models and the learning capability of neural networks in a single system. Two combinations of input data were used in the analyses to predict scour depth: the first input combination involves dimensional parameters such as wave height, wave period, and water depth, while the second combination contains nondimensional numbers including the Reynolds number, the Keulegan–Carpenter number, the Shields parameter and the sediment number. The test results show that ANFIS performs better than the existing empirical formulae. The ANFIS predicts scour depth better when it is trained with the original (dimensional) rather than the nondimensional data. The depth of scour was predicted more accurately than its width. A sensitivity analysis showed that scour depth is governed mainly by the Keulegan–Carpenter number, and wave height has a greater influence on scour depth than the other independent parameters. 相似文献
2.
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
3.
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
4.
A fuzzy inference system (FIS) and a hybrid adaptive network-based fuzzy inference system (ANFIS), which combines a fuzzy inference system and a neural network, are used to predict and model longshore sediment transport (LST). The measurement data (field and experimental data) obtained from Kamphuis [1] and Smith et al. [2] were used to develop the model. The FIS and ANFIS models employ five inputs (breaking wave height, breaking wave angle, slope at the breaking point, peak wave period and median grain size) and one output (longshore sediment transport rate). The criteria used to measure the performances of the models include the bias, the root mean square error, the scatter index and the coefficients of determination and correlation. The results indicate that the ANFIS model is superior to the FIS model for predicting LST rates. To verify the ANFIS model, the model was applied to the Karaburun coastal region, which is located along the southwestern coast of the Black Sea. The LST rates obtained from the ANFIS model were compared with the field measurements, the CERC [3] formula, the Kamphuis [1] formula and the numerical model (LITPACK). The percentages of error between the measured rates and the calculated LST rates based on the ANFIS method, the CERC formula (Ksig = 0.39), the calibrated CERC formula (Ksig = 0.08), the Kamphuis [1] formula and the numerical model (LITPACK) are 6.5%, 413.9%, 6.9%, 15.3% and 18.1%, respectively. The comparison of the results suggests that the ANFIS model is superior to the FIS model for predicting LST rates and performs significantly better than the tested empirical formulas and the numerical model. 相似文献
5.
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
6.
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
7.
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
8.
带周期项的海平面变化灰色分析模型及广西海平面变化分析 总被引：7，自引：0，他引：7
提出了一种带周期项的海平面变化灰色分析模型.该模型保持了GM(1,1)模型能较好反应海平面变化趋势的优点,不仅能求出海平面变化速率,还能方便求出海平面变化的加速度,同时,该模型能较好的模拟海平面变化中的周期现象,从而克服了GM(1,1)不能预报周期性显著的月平均海面的缺点,并提高了预报精度.模型用于广西沿岸海平面变化分析,结果表明北海、涸洲、白龙尾3站的相对海平面上升速率分别为1.67、2.51、0.89mm/a;石头埠相对海平面呈下降趋势,下降速率为0.5～1.0mm/a;广西沿岸绝对海平面上升速率为2.0mm/a.和线性趋势项与周期项叠加的海平面分析模型相比,两者模拟精度相当. 相似文献
9.
Guillem Chust Ainhoa Caballero Marta Marcos Pedro Liria Carlos Hernández Ángel Borja 《Estuarine, Coastal and Shelf Science》2010
Global climate models have predicted a rise on mean sea level of between 0.18 m and 0.59 m by the end of the 21st Century, with high regional variability. The objectives of this study are to estimate sea level changes in the Bay of Biscay during this century, and to assess the impacts of any change on Basque coastal habitats and infrastructures. Hence, ocean temperature projections for three climate scenarios, provided by several atmosphere–ocean coupled general climate models, have been extracted for the Bay of Biscay; these are used to estimate thermosteric sea level variations. The results show that, from 2001 to 2099, sea level within the Bay of Biscay will increase by between 28.5 and 48.7 cm, as a result of regional thermal expansion and global ice-melting, under scenarios A1B and A2 of the Intergovernmental Panel on Climate Change. A high-resolution digital terrain model, extracted from LiDAR, data was used to evaluate the potential impact of the estimated sea level rise to 9 coastal and estuarine habitats: sandy beaches and muds, vegetated dunes, shingle beaches, sea cliffs and supralittoral rock, wetlands and saltmarshes, terrestrial habitats, artificial land, piers, and water surfaces. The projected sea level rise of 48.7 cm was added to the high tide level of the coast studied, to generate a flood risk map of the coastal and estuarine areas. The results indicate that 110.8 ha of the supralittoral area will be affected by the end of the 21st Century; these are concentrated within the estuaries, with terrestrial and artificial habitats being the most affected. Sandy beaches are expected to undergo mean shoreline retreats of between 25% and 40%, of their width. The risk assessment of the areas and habitats that will be affected, as a consequence of the sea level rise, is potentially useful for local management to adopt adaptation measures to global climate change. 相似文献
10.
Sound from an airborne source travels to a receiver beneath the sea surface via a geometric path that is most simply described using ray theory, where the atmosphere and the sea are assumed to be isospeed sound propagation media separated by a planar surface (the air-sea interface). This theoretical approach leads to the development of a time-frequency model for the signal received by a single underwater acoustic sensor and a time-delay model for the signals received by a pair of spatially separated underwater acoustic sensors. The validity of these models is verified using spatially averaged experimental data recorded from a linear array of hydrophones during various transits of a turboprop aircraft. The same approach is used to solve the inverse time-frequency problem, that is, estimation of the aircraft's speed, altitude, and propeller blade rate given the observed variation with time of the instantaneous frequency of the received signal. Similarly, the inverse time-delay problem is considered whereby the speed and altitude of the aircraft are estimated using the differential time-of-arrival information from each of two adjacent pairs of widely spaced hydrophones (with one hydrophone being common to each pair). It is found that the solutions to each of the inverse problems provide reliable estimates of the speed and altitude of the aircraft, with the inverse time-frequency method also providing an estimate that closely matches the actual propeller blade rate 相似文献
11.
在海温预报中引入混沌理论,将相空间重构与模糊神经网络相结合,提出了海温垂直建模预测模型。通过相空间重构,把海温时间序列拓展为多维序列,而多维序列包含着各态历经的信息,从而挖掘出了丰富的海温变化空间的信息,有利于模糊神经网络的训练。利用建立好的模糊神经网络模型,较好地对海温的垂直结构进行了建模、训练和预测。实际的预测结果表明,该模型预报精度较高,超前1~5个月的预测值的相对误差均控制在10%以内,预测结果可以为业务工作提供一定的参考与借鉴。 相似文献
12.
Tide gauges distributed all over the world provide valuable information for monitoring mean sea level changes. The statistical models used in estimating sea level change from the tide gauge data assume implicitly that the random model components are stationary in variance. We show that for a large number of global tide gauge data this is not the case for the seasonal part using a variate-differencing algorithm. This finding is important for assessing the reliability of the present estimates of mean sea level changes because nonstationarity of the data may have marked impact on the sea level rate estimates, especially, for the data from short records. 相似文献
13.
A~as~Sof~~LIngeneral,sealevelisresolvedintOatrendtermplusaPeriedictermintheanalysisofsealevelvdriations(haetal.1996;ZuoandChen,1996;QinandLi,1997;Zhengetal.,1993;RenandZhang,1993),namely,thetimeequencesofmonthlyorannualmeansealevely(o)(t)canbeexpr~asy(o)(t)=T(o)(t) p(o)(t) X(o)(t) .(o)(t),(l)whereT(o)(t)isadefinitetrendterm;p(o)(t)isadefiniteperiedicterm;X(o)(t)isatimeseriesofrandomterm;a(o)(t)iswhitenoise.Thefunctionstructuresofthetrendtermaregenerallyunknown,whiledeterminingthetrendter… 相似文献
14.
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
15.
Tide gauges distributed all over the world provide valuable information for monitoring mean sea level changes. The statistical models used in estimating sea level change from the tide gauge data assume implicitly that the random model components are stationary in variance. We show that for a large number of global tide gauge data this is not the case for the seasonal part using a variate-differencing algorithm. This finding is important for assessing the reliability of the present estimates of mean sea level changes because nonstationarity of the data may have marked impact on the sea level rate estimates, especially, for the data from short records. 相似文献
16.
北极海冰冰盖自20世纪以来经历了前所未有的缩减,这使得北极海冰异常对大气环流的反馈作用日益显现。尽管目前的气候模式模拟北极海冰均为减少的趋势,但各模式间仍然存在较大的分散性。为了评估模式对于北极海冰变化及其气候效应的模拟能力,我们将海冰线性趋势和年际异常两者结合起来构造了一种合理的衡量指标。我们还强调巴伦支与卡拉海的重要性,因为前人研究证明此区域海冰异常是近年来影响大尺度大气环流变异的关键因子。根据我们设定的标准,CMIP5模式对海冰的模拟可被归为三种类型。这三组多模式集合平均之间存在巨大的差异,验证了这种分组方法的合理性。此外,我们还进一步探讨了造成模式海冰模拟能力差别的潜在物理因子。结果表明模式所采用的臭氧资料集对海冰模拟能力有显著的影响。 相似文献
17.
基于高光谱遥感的渤海海冰厚度半经验模型 总被引：1，自引：0，他引：1
Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness(SEMSIT) that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands(spectral dimension information) are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided. 相似文献
18.
海洋环流模式中不同近似假设下的海表高度 总被引：1，自引：1，他引：1
Boussinesq近似是现代海洋环流模式中经常采用的假设，但随着海洋模式的不断发展完善以及气候研究应用的需要，有必要估算Boussinesq近似造成的模式误差。分别利用一个非Boussinesq近似的海洋模式与另一个结构相同且采用Boussinesq近似的模式计算海表高度，并同时利用模式预报的温度、盐度资料计算了比容异常高度。分析结果显示，这3种不同定义的海表高度无论空间结构，还是时间演变，都基本类似，尤其在热带海区最接近，差值≤1cm。Boussinesq近似意味着在模式中以体积守恒代替质量守恒，通常的做法是对其进行简单的质量补偿来保持质量守恒。比较说明，以质量补偿方法进行的高度订正对减小Boussinesq近似带来的误差没有本质的意义。 相似文献
19.
中国沿岸现代海平面变化及未来趋势分析 总被引：2，自引：0，他引：2
本文用线性回归分析方法，分１９８５年以前和１９９２年以前两个时段，对我国沿岸２５个验潮站近百年来的海平面资料进行了系统分析，计算了两个时段相对海平面变化的年速率和平均海面高度，论述了海平面变化的主要控制因素，并对未来海平面变化趋势进行了预测。计算结果表明，近百年来我国沿岸相对海平面在总体上不但持续上升，而且近年来上升速率普遍加快；根据海平面变化的主要控制因素变化趋向，预计到下世纪中叶前后，全球性海平面大幅度上升的可能性不大，我国沿岸区域性海乎面平均上升幅度不超过１５ｃｍ，不同岸段因地壳升降差异性大而有较大差别。 相似文献
20.
使用ROMS(regional oceanic modeling system)模式模拟了40年的渤黄东海温盐流,数据包括三维的温度、盐度、流速、流向和海表高度,同时包含了逐小时的潮汐信息。将模拟结果与观测资料和卫星反演数据进行对比,检验了模式准确性。整体上,模式模拟的水位与近岸观测值基本一致,能够准确再现风产生的增水;模式较为准确的再现了渤黄东海的温度分布,在深水区模拟的温盐剖面与观测值基本一致;模式模拟渤黄东海区域的海表高度和海表流与卫星反演结果相比偏小,但分布趋势相近。模式结果可以为研究气候变化对水位的影响和黄海暖舌的扩散过程等现象提供数据支持。 相似文献