共查询到20条相似文献，搜索用时 15 毫秒
1.
《Marine Geology》2004,203(1-2):185-197
Spatially and temporally extensive nearshore bathymetric datasets have recently been analyzed with complex principal component analysis (CPCA) to extract propagating spatial patterns that constitute most to the dominant lower-dimensional structure in the datasets. CPCA assumes this structure to be linear, which may be an oversimplification of the true structure as the nearshore is a strongly non-linear system. Here we investigate whether a neural network-based circular non-linear principal component analysis (NLPCA.cir) provides a more representative approximation of the underlying lower-dimensional structure of nearshore depth data than possible with CPCA. To that end, NLPCA.cir was applied to three datasets characterized by interannual offshore-directed sandbar behavior, coming from Egmond (The Netherlands), Hasaki Coast (Japan) and Duck (North Carolina, USA). The main difference between these datasets is the temporal variation in sandbar amplitude, which is smallest at Egmond and largest at Duck. We find that the first NLPCA.cir mode 1 at Egmond and Hasaki leads to a more complete characterization of the lower-dimensional data structure than possible with CPCA (i.e. the underlying low-dimensional structure of bathymetric data at Egmond and Hasaki is indeed non-linear). This is not the case at Duck, where the temporal variations in sandbar amplitude are so large that the non-linear lower-dimensional structure in the data, should it exist, is no longer visually apparent and can no longer be detected by NLPCA.cir. This suggests that a simple visual check of the data suffices to decide whether one should resort to NLPCA.cir or to the more simple and computationally quicker CPCA. 相似文献
2.
《中国海洋工程》2020,(4)
The study of mooring forces is an important issue in marine engineering and offshore structures. Although being widely applied in mooring system, numerical simulations suffer from difficulties in their multivariate and nonlinear modeling. Data-driven model is employed in this paper to predict the mooring forces in different lines, which is a new attempt to study the mooring forces. The height and period of regular wave, length of berth, ship load, draft and rolling period are considered as potential influencing factors. Input variables are determined using mutual information(MI) and principal component analysis(PCA), and imported to an artificial neural network(NN) model for prediction. With study case of 200 and 300 thousand tons ships experimental data obtained in Dalian University of Technology, MI is found to be more appropriate to provide effective input variables than PCA. Although the three factors regarding ship characteristics are highly correlated, it is recommended to input all of them to the NN model.The accuracy of predicting aft spring line force attains as high as 91.2%. The present paper demonstrates the feasibility of MI-NN model in mapping the mooring forces and their influencing factors. 相似文献
3.
4.
SONG Xingyu HUANG Liangmin ZHANG Jianlin YIN Kedong LIU Sheng TAN Yehui YIN Jianqiang 《海洋学报(英文版)》2011,30(4):60-74
Chemometric approach based on principal component analysis (PCA) was utilized to examine the spatial variances of environmental and ecological characteristics in the Zhujiang River (Pearl River) Estuary and adjacent waters (ZREAW) in the South China Sea. The PCA result shows that the ZREAW can be divided into different zones according to the principal components and geographical locations of the study stations, and indicates that there are distinct regional variances on environmental features and the corresponding phytoplankton biomass and community structures among different areas. The spatial distribution of ecological features was implied to be influenced by various degrees of the different water resources, such as the Pearl River discharges, the coastal current and the oceanic water from the South China Sea. The variation of the biomass maximum zone and the complex impacts on the spatial distributions of phytoplankton biomass and production were also evaluated. 相似文献
5.
A rotated coordinates inversion algorithm is used on subsets of the Inversion Techniques 2001 Geoacoustic Workshop data, to which white Gaussian noise is added. The resulting data sets are equivalent to noisy broad-band signals received on a horizontal line array (HLA) during a single integration time interval. The inversions are performed using a technique called systematic decoupling using rotated coordinates (SDRC), which expands the original idea of rotated coordinates by using multiple sets of rotated coordinates, each corresponding to a different set of bounds, to systematically decouple the unknowns in a series of efficient simulated annealing inversions. The cost function minimized in the inversion is based on the coherent broad-band correlation between data and model cross spectra, which increases the coherence gain of the signal relative to incoherent noise. Using the coherent broad-band cost function with sparse HLA-like data sets, the SDRC inversion method yields good estimates for the sensitive environmental parameters for signal-to-noise ratios as low as -15 dB. 相似文献
6.
The paper presents an approach towards a medium-term (decades) modelling of water levels and currents in a shallow tidal sea by means of combined hydrodynamic and neural network models. The two-dimensional version of the hydrodynamic model Delft3D, forced with realistic water level and wind fields, is used to produce a two-year-database of water levels and currents in the study area. The linear principal component analysis (PCA) of the results is performed to reveal dominating spatial patterns in the analyzed dataset and to significantly reduce the dimensionality of the data. It is shown that only a few principal components (PCs) are necessary to reconstruct the data with high accuracy (over 95% of the original variance). Feed-forward neural networks are set up and trained to effectively simulate the leading PCs based on water level and wind speed and direction time series in a single, arbitrarily chosen point in the study area. Assuming that the spatial modes resulting from the PCA are ‘universally’ applicable to the data from time periods not modelled with Delft3D, the trained neural networks can be used to very effectively and reliably simulate temporal and spatial variability of water levels and currents in the study area. The approach is shown to be able to accurately reproduce statistical distribution of water levels and currents in various locations inside the study area and thus can be viewed as a reliable complementary tool e.g., for computationally expensive hydrodynamic modelling. Finally, a detailed analysis of the leading PCs is performed to estimate the role of tidal forcing and wind (including its seasonal and annual variability) in shaping the water level and current climate in the study area. 相似文献
7.
主成分分析法在海洋工程系统风险分析中的应用 总被引：6，自引：0，他引：6
风险分析作为一门新的应用技术，正在不断地为更多的人们所认识。它不仅在重大项目的投资分析中起着不可缺少的重要作用，并且正在被广泛的应用于军事、环境、科学和工程等领域中。但是，如何在系统风险分析中，特别是在工程系统风险分析中对已识别出的风险事件进行量化分析、找出主要风险事件对系统的影响及对子系统的风险程度进行比较和分析仍是一个重要的课题。这个问题的解决将使风险分析更接近工程实际、更具有实际的指导意义。文章介绍了主成分分析法的原理和实施步骤，并通过实例介绍了主成分分析法在系统风险分析中的应用。在系统风险分析中使用主成分分析法，可以对系统中各子系统的风险程度进行客观的评估，并且可以对风险事件进行聚类分析，使风险控制更容易实施，并且使风险分析更接近工程实际、更具有指导意义。文章中还指出了在使用主成分分析法时应注意的问题。 相似文献
8.
Three stations located in the Laurentian Trough were investigated three times during the summer production maximum (in June-July 1990; COUPPB90-1 cruise). A range of multivariate analyses was carried out to extract major physical-biological features in a large data set covering the whole cruise. This study presents the advantages of combining conventional principal component analysis (PCA) with a three-way data analysis. PCA provides the centre of gravity of the different locations for each variable and each sample during the cruise without temporal information. Three-way data analysis produces the trajectory of the different site/time locations for each variable and each sample, and clarifies spatial and temporal relationships between variables. The results reproduce the main patterns of the lower St Lawrence Estuary during the summer plankton bloom and indicate an upstream movement of warm and less saline waters associated with surface production. Apparently these large changes at the surface are not reflected in the deeper layers. Cluster analysis suggests that autotrophic and heterotrophic activities are decoupled when production is low and are correlated only when production increases. 相似文献
9.
A total of 160 barramundi's (Lates calcarifer Bloch, 1790) sampled from four rivers (Tentulia, Balaswar, Bakkhali, and Andarmanik) along the southern coastal region of Bangladesh were investigated in terms of morphometric characters to reveal the intraspecific variation. Twenty-five morphometric measurements were extracted using the conventional method and subjected to multivariate analyses (i.e., principal component analysis (PCA), discriminate function analysis (DFA), cluster analysis (CA)) to distinguish individuals from different rivers. The result demonstrated that twenty-two out of 25 measurements was statistically significant (Univariate ANOVA) among all four populations. PCA analysis of morphometric characters resulted in two principal components, PC I and PCⅡ which accounted for 79.25% and 4.28% of the total data variance. PC I-PC Ⅱ plot explained 83.53% of total variance differentiated the population of L. calcarifer into two groups. Discriminate analysis correctly classified about 88.1% of the examined fish into the four areas. The UPGMA dendrogram showed that Bakkhali populations were the most morphologically different populations in comparison to other populations, while Andarmanik and Balaswar populations were very close to each other. The strong morphometric variation between Bakkhali and Tentulia, Andarmanik and Balaswar was observed in the present study, suggested the evidence of the separate stock population of barramundi in these locations, which might require distinct stock management strategies for resource sustainability in the waters of southern Bangladesh. However, if these findings are supported by further molecular markers and geometric morphometry, this would be a strong indication of different stocks of this population in the four rivers of southern Bangladesh. 相似文献
10.
斑尾复鰕虎鱼群体的形态学比较 总被引：3，自引：0，他引：3
对采自丹东、大连、东营、青岛、赣榆、舟山、厦门7个斑尾复鰕虎鱼群体的可量性状和可数性状进行了主成分分析、聚类分析、判别分析和单因子方差分析.主成分分析和聚类分析结果显示东营和赣榆群体为一组,而其它群体为另一组.2组差异极为显著.主成分分析构建了4个主成分,其贡献率分别为41.30%,8.92%,8.08%和7.31%,累计贡献率为65.60%.第一主成分主要受尾柄长/尾柄高、体高/体长、头长/体长和吻至背鳍起点/体长的影响.判别分析也可将两组群体分开,东营和赣榆组与另一组没有误判,对于样本所属海域的判别,其中东营为100%,最低为青岛群体66.7%,综合判别率为85.5%;选取贡献较大的14个特征值建立判别公式,利用判别公式计算各群体的差别准确率为62.1%～100%.对形态特征参数进行单因子方差分析,计算差异系数,根据Mayr等提出的75%规则,认为两组群体已达到亚种水平,而两组群体内的形态差异为种内不同群体间的差异. 相似文献
11.
The study investigates perspectives of the parameter estimation problem with the adjoint method in eddy-resolving models. Sensitivity to initial conditions resulting from the chaotic nature of this type of model limits the direct application of the adjoint method by predictability. Prolonging the period of assimilation is accompanied by the appearance of an increasing number of secondary minima of the cost function that prevents the convergence of this method. In the framework of the Lorenz model it is shown that averaged quantities are suitable for describing invariant properties, and that secondary minima are for this type of data transformed into stochastic deviations. An adjoint method suitable for the assimilation of statistical characteristics of data and applicable on time scales beyond the predictability limit is presented. The approach assumes a greater predictability for averaged quantities. The adjoint to a prognostic model for statistical moments is employed for calculating cost function gradients that ignore the fine structure resulting from secondary minima. Coarse resolution versions of eddy-resolving models are used for this purpose. Identical twin experiments are performed with a quasigeostrophic model to evaluate the performance and limitations of this approach in improving models by estimating parameters. The wind stress curl is estimated from a simulated mean stream function. A very simple parameterization scheme for the assimilation of second-order moments is shown to permit the estimation of gradients that perform efficiently in minimizing cost functions. 相似文献
12.
河口水流是河口生态环境、河道演变、物质输运等物理过程的根本动力。由于径流、潮波、地形以及气象等因素的影响,河口水流呈现复杂的三维结构。其中既包括淡水注入形成的余流,也包括周期性的潮流、风生流、斜压流及河口非线性作用导致的流动等。为探究河口水流的组成及其潮内变化,基于瓯江口实测资料,利用主成分分析（Principal Component Analysis, PCA）法对河口水流进行分解,探讨了PCA法对河口水流的分解性能及斜压分潮流的高频特征。研究认为,PCA法在河口水流结构研究中既可采用原始数据操作亦可用标准化的数据进行计算。PCA法可分解出斜压成分（河口重力环流型结构）,但不能将正压成分（径流和潮流）分开,径流和潮流二者综合作用的结果体现在主成分的得分之中。主成分的取舍应根据水流结构和累计解释方差综合判断,不宜仅依据累计解释方差。河口斜压流动具有明显的高频特征,近似呈1/4日分潮的周期。 相似文献
13.
基于主成分和BP神经网络的智利竹筴鱼渔场预报模型研究 总被引：3，自引：0，他引：3
东南太平洋智利竹筴鱼Trachurus murphyi是我国大型拖网渔船队的重要捕捞对象。准确预报中心渔场是提高渔业生产能力的重要工作。本文根据2003—2009年我国船队在东南太平洋海域捕捞智利竹筴鱼的渔捞日志数据,结合海洋遥感获得的海表温度(SST)和海面高度(SSH)等海洋环境因子,利用主成分和BP神经网络方法对智利竹筴鱼中心渔场预报模型进行了研究。研究利用主成分分析法(PCA)得到累计贡献率在90%以上样本的主成分,综合考虑模型测试的精度与速度,基于原始样本和经PCA处理后的主成分分别建立了BP模型,其最优BP模型结构分别为5∶10∶1和3∶7∶1。研究结果表明,经PCA处理后的主成分所建立的BP神经网络模型在训练结果和测试结果上均要优于用原始样本建立的BP神经网络模型,两者的预报准确率分别为67%和60%。 相似文献
14.
Multivariate analysis of fluorescence and source identification of dissolved organic matter in Jiaozhou Bay, China 总被引：1，自引：0，他引：1
Hierarchical clustering analysis and principal component analysis (PCA) methods were used to assess the similarities and dissimilarities of the entire Excitation-emission matrix spectroscopy (EEMs) data sets of samples collected from Jiaozhou Bay, China. The results demonstrate that multivariate analysis facilitates the complex data treatment and spectral sorting processes, and also enhances the probability to reveal otherwise hidden information concerning the chemical characteristics of the dissolved organic matter (DOM). The distribution of different water samples as revealed by multivariate results has been used to track the movement of DOM material in the study area, and the interpretation is supported by the results obtained from the numerical simulation model of substance tracing technique, which show that the substance discharged by Haibo River can be distributed in Jiaozhou Bay. 相似文献
15.
Shanthi R. Poornima D. Raja K. Sarangi R. K. Saravanakumar A. Thangaradjou T. 《海洋学报(英文版)》2015,34(6):94-100
Seasonal and inter-annual variability of hydrological parameters and its impact on chlorophyll distribution was studied from January 2009 to December 2011 at four coastal stations along the southwest Bay of Bengal. Statistical analysis(principal component analysis(PCA), two-way analysis of variance(ANOVA) and correlation analysis)showed the significant impact of hydrological parameters on chlorophyll distribution in the study area. The ranges of different parameters recorded were 23.8–33.8°C(SST), 4.00–36.00(salinity), 7.0–9.2(p H), 4.41–8.32 mg/L(dissolved oxygen), 0.04–2.45 μmol/L(nitrite), 0.33–16.10 μmol/L(nitrate), 0.02–2.51 μmol/L(ammonia),0.04–3.32 μmol/L(inorganic phosphate), 10.09–85.28 μmol/L(reactive silicate) and 0.04–13.8 μg/L(chlorophyll).PCA analysis carried out for different seasons found variations in the relationship between physico-chemical parameters and chlorophyll in which nitrate and chlorophyll were positively loaded at PC1(principal component1) during spring inter-monsoon and at PC2(principal component 2) during other seasons. Likewise correlation analysis also showed significant positive relationship between chlorophyll and nutrients especially with nitrate(r=0.734). Distribution of hydrobiological parameters between stations and distances was significantly varying as evidenced from the ANOVA results. The study found that the spatial and temporal distribution of chlorophyll was highly dependent on the availability of nutrients especially, nitrate in the southwest Bay of Bengal coastal waters. 相似文献
16.
HY-1/COCTS波段信息含量与波段相关性分析--以辽东湾为例 总被引：2，自引：0，他引：2
以2003年4月辽东湾的HY-1／COCTS数据为例,采用主成分分析法和相关分析法,从光谱的不同结构、主成分、相关性等方面来揭示COCTS图像的光谱信息。COCTS的10个波段中,9,10两个波段与其他几个波段相比,包含的信息较丰富。在其他的8个波段中,6,7波段的信息较为丰富,6波段对主成分的贡献大。这8个波段与9波段和10波段的相关性为负相关,表明它们间的重叠少。5波段与1波段、7波段与6波段、6波段与2波段的相关性较高,这说明这些数据间的彼此重叠较多,故在进行波段图像的假彩色合成时一般以8,4,3或8,4,1波段的组合为佳。以OIF指数来检查融合方法的可行性。 相似文献
17.
Septriono Hari Nugroho Purna Sulastya Putra Eko Yulianto Dardji Noeradi 《Marine Georesources & Geotechnology》2018,36(8):907-917
A study of sediment dynamics has been conducted on the Tarakan sub-basin, North Kalimantan, Indonesia using multivariate analysis. Multivariate statistical techniques can be used to determine sediment with similar characteristics and be a good proxy to recognize sedimentary facies and depositional environment. These methods have been applied to characterize the sedimentary facies at Tarakan sub-basin. A total of 23 samples were taken from several locations on the different depths of 56–2554?m that represent varieties of environment. The study is interpreted using compositional data analysis associated with a grain size trend analysis (GSTA), principal component analysis (PCA), and cluster analysis (CA). The GSTA value showed the dominance of poorly sorted silt indicating that the sediment is mostly deposited in low-energy depositional. The CA and the PCA determined three distinct sedimentary facies: shelf facies, upper bathyal facies, and lower bathyal facies. The facies were grouped as similar sediment and depositional environment. Sedimentological variables applied in the characterization were described to be an important tool for the interpretation of depositional environments – indirectly showing hydrodynamic energy. 相似文献
18.
19.
东南太平洋秘鲁鳀资源量预报模型的构建 总被引：2，自引：0，他引：2
秘鲁鳀（Engraulis ringens）是栖息于东南太平洋沿岸的一种小型中上层鱼类。有效地对秘鲁鳀资源量进行预报将有助于为我国鱼粉进口企业提供决策支撑。为此,本研究结合秘鲁鳀生物（上一个渔汛季度的资源量、渔获物中的幼鱼比例）和环境（渔场水温和nino1+2区的温度距平）因素及捕捞量为预报因子,利用主成分分析和多元线性回归的方法对17个渔汛季度（2006年至2014年第一渔汛季度）秘鲁鳀的资源量建立预报模型并利用主成分分析的结果对影响秘鲁鳀资源变动的因子进行初步评价。研究表明,随着时间的推移样本量的增加,模型拟合资源量与真实资源量的平均相对误差逐渐下降,拟合资源量序列与真实资源量序列的相关系数逐渐增加。最终模型5（建模数据为2006-2013年第二渔汛季度的数据,验证数据为2014年第一渔汛季度的数据）能够很好地拟合出秘鲁鳀资源量的大小及变动趋势拟合资源量序列与真实资源量序列的相关系数为0.861;拟合资源量序列与真实资源量序列的平均相对误差为12%;预报得到的2014年第一渔汛季度的数据与真实值相比,相对误差为1%。结合主成分分析的结果对影响秘鲁鳀资源变动的因素进行评价结果表明,第一主成分的方差贡献率为46%,其中环境因素占据了最大的载荷;第二主成分的方差贡献率为23%,同样环境因素占据了最大载荷,但是,排名第二和第三的因素分别是上一个渔汛季度的资源量和捕捞量,其载荷相当;第三和第四主成分的方差贡献率分别为9%和7%,其中幼鱼比例占的载荷最大。根据各主成分得分序列与资源量序列的相关系数结果,环境因子对秘鲁鳀资源变动有重要影响。 相似文献
20.
Evangelos Alevizos Mirjam Snellen Dick Simons Kerstin Siemes Jens Greinert 《Marine Geophysical Researches》2018,39(1-2):289-306
This study applies three classification methods exploiting the angular dependence of acoustic seafloor backscatter along with high resolution sub-bottom profiling for seafloor sediment characterization in the Eckernförde Bay, Baltic Sea Germany. This area is well suited for acoustic backscatter studies due to its shallowness, its smooth bathymetry and the presence of a wide range of sediment types. Backscatter data were acquired using a Seabeam1180 (180 kHz) multibeam echosounder and sub-bottom profiler data were recorded using a SES-2000 parametric sonar transmitting 6 and 12 kHz. The high density of seafloor soundings allowed extracting backscatter layers for five beam angles over a large part of the surveyed area. A Bayesian probability method was employed for sediment classification based on the backscatter variability at a single incidence angle, whereas Maximum Likelihood Classification (MLC) and Principal Components Analysis (PCA) were applied to the multi-angle layers. The Bayesian approach was used for identifying the optimum number of acoustic classes because cluster validation is carried out prior to class assignment and class outputs are ordinal categorical values. The method is based on the principle that backscatter values from a single incidence angle express a normal distribution for a particular sediment type. The resulting Bayesian classes were well correlated to median grain sizes and the percentage of coarse material. The MLC method uses angular response information from five layers of training areas extracted from the Bayesian classification map. The subsequent PCA analysis is based on the transformation of these five layers into two principal components that comprise most of the data variability. These principal components were clustered in five classes after running an external cluster validation test. In general both methods MLC and PCA, separated the various sediment types effectively, showing good agreement (kappa >0.7) with the Bayesian approach which also correlates well with ground truth data (r2?>?0.7). In addition, sub-bottom data were used in conjunction with the Bayesian classification results to characterize acoustic classes with respect to their geological and stratigraphic interpretation. The joined interpretation of seafloor and sub-seafloor data sets proved to be an efficient approach for a better understanding of seafloor backscatter patchiness and to discriminate acoustically similar classes in different geological/bathymetric settings. 相似文献