基于PCA的地震官方微博影响力评价研究

随着政务微博用户规模及影响力的不断提升,微博作为地震部门传播平台,在地震信息传播方面发挥着巨大的作用。本文在充分考虑地震部门行业特点的基础上,对30个地震官方微博数据进行收集,利用主成分分析法(PCA)归纳出3种地震微博的主要影响指标,即服务力主成分、交互力主成分和创作力主成分,并由此构建出地震官方微博影响力评估指标体系,在此基础上计算得出各地震官方微博的主成分指标得分和影响力综合得分,最后根据得分情况对地震官方微博影响力提出了具可行性的提升策略。     

Modelling of a medium-term dynamics in a shallow tidal sea, based on combined physical and neural network methods

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.     

Texture-based identification of urban slums in Hyderabad, India using remote sensing data

This paper outlines a methodology to identify informal settlements out of high resolution satellite imagery using the concept of lacunarity. Principal component analysis and line detection algorithms were applied alternatively to obtain a high resolution binary representation of the city of Hyderabad, India and used to calculate lacunarity values over a 60 × 60 m grid. A number of ground truthing areas were used to classify the resulting datasets and to identify lacunarity ranges which are typical for settlement types that combine high density housing and small dwelling size - features characteristic for urban slums in India. It was discovered that the line detection algorithm is advantageous over principal component analysis in providing suitable binary datasets for lacunarity analysis as it is less sensitive to spectral variability within mosaicked imagery. The resulting slum location map constitutes an efficient tool in identifying particularly overcrowded areas of the city and can be used as a reliable source in vulnerability and resilience assessments at a later stage. The proposed methodology allows for rapid analysis and comparison of multi-temporal data and can be applied on many developing urban agglomerations around the world.     

Chemical characteristics of wet precipitation at an urban site of Guangzhou, South China

The pH variation and chemical characteristics of rainwater were investigated from January 2006 to December 2006 at an urban site of Guangzhou, South China. The rainwater was typically acidic with a volume-weighted mean pH value of 4.49, which ranged from 3.52 to 6.28. The volume-weighted mean equivalent concentration of components followed the order: SO₄²⁻ > Ca²⁺ > Cl⁻ > NH₄⁺ > Na⁺ > NO₃⁻ > K⁺ > Mg²⁺ > F⁻, indicating that SO₄²⁻, Cl⁻ and NO₃⁻ were the main anions, while Ca²⁺ and NH₄⁺, were the main cations. Ca²⁺ and NH₄⁺ were major neutralization constituents of the precipitation. Furthermore, correlation analysis and principal component analysis method were performed to identify possible common sources of major ions. Sources of the major ions were assessed based on enrichment factor method.     

Classification of natural mineral and spring bottled waters of Portugal using Principal Component Analysis

Considering its area, Portugal is one of the world's richest countries in mineral and spring waters. There are 33 different types of bottled water, 18 of which are classified as natural mineral water and the remaining as spring water. The majority of these waters are of low mineralisation in comparison to most European bottled waters.     

基于主成分回归的阿克苏市城市需水量预测

科学地预测城市需水量对城市的发展具有十分重要的意义。城市需水量受到多重因素的影响,各因素之间的相关性较大,从而导致一些预测计算结果失真,使一些公式的适用性不是很强。为此,采用主成分回归建模,借助主成分分析与典型相关分析的思路,有效地解决了各因子之间的相关问题,建立了城市需水量预报模型。结果表明．模型的拟合和预报精度均较好。     

山东省飞机增雨降水区分级预报方法研究

利用1997～2000年春秋季山东主要降水天气系统中尺度客观分析和降水模拟资料，统计分析了与降水密切相关的15个因子，并对因子的稳定性进行检验。用全部入选因子的前5个主成分作为新的预报因子，建立全省25个区域0～6h和0～12h平均降水量的3类判别方程，判别效果较好。以此方法为基础建立的决策系统为增雨作业指挥提供了实用性技术工具。     

地震危险综合评判的分量分析方法

地震危险性评判，通常需要将多种方法的结果加以综合。为了去除各种方法中可能的重复信息，并对各种方法对危险性评判结论的重要程度进行合理的度量，使综合的结果更加合理，我们引入了分量分析方法。本文首先介绍了分量分析方法的基本原理，其次阐述了该方法在地震危险性综合评判中的应用，最后给出了华北北部中长期地震危险性预测的一个计算实例。     

用主成分法求解秩亏自由网的研究

结合多数平差和主成分估计理论,导出误差方程中含多重共线性关系时求解未知数的公式,并证明主成分估计的解是最小二乘最小范效解。由此,将主成分估计推广到秩亏自由网平差中,导出求解未知多数及其协因数的公式,并据主成分估计演绎出用附有条件的参数平差法求解秩亏自由网的条件方程（基准在达式）。     

DETECTING AND ADJUSTING FOR NON-LINEARITIES IN CALIBRATION OF NEAR-INFRARED DATA USING PRINCIPAL COMPONENTS

A new regression method for non-linear near-infrared spectroscopic data is proposed.The technique isbased on a model which is linear in the principal components and simple functions(squares and products)of them.Added variable plots are used to determine which squares and products to incorporate into themodel.The regression coefficients are estimated by a Stein estimate which shrinks towards the estimatedetermined by the first several principal components and the selected non-linear terms.The technique isnot computationally intensive and is appropriate for routine predictions of chemical concentrations.Themethod is tested on three data sets and in all cases gives more accurate predictions than does linearprincipal components regression.