一种新的利用单基站GNSS反演大气剖面的技术

在传统的模型参数法反演大气折射率的基础上提出一种新的模型参数法,并建立一种新的估算对流层高度的方法。将探空产品计算的大气折射率视为真值,选择2016-07中国及周边区域8个IGS测站进行试算,统计结果显示,与传统的模型参数法相比,新的模型参数法反演的大气折射率更接近真值。     

广义补偿最小二乘估计及其统计性质

分析了半参数模型最小二乘解的特点,特别指出矩阵BTPB病态或奇异时,补偿最小二乘将会失效。基于线形模型岭估计原理,提出了半参数广义补偿最小二乘估计法,给出了其估计公式及其统计性质,并通过算例分析得出了相应的结论。     

生长模型驱动的单株杉木三维动态模拟

虚拟植物是一种潜在、有力的植物分析辅助工具,而单株木生长模拟是森林生态系统模拟的基础。为了动态模拟杉木生长发育过程,提出了参数化单株木三维形态结构建模和与距离无关的单木生长模型的集成方法,其主要过程为：首先,根据杉木的形态结构特征,采用参数化建模方法建立特定生长阶段的三维静态模型;其次,根据生长模型预测不同年龄杉木的树高、胸径、枝下高和分枝轮数,并与树木三维静态模型几何描述参数建立联动;最后,采用参数曲线调整干径和枝径变化、枝条长度、分枝角等形态结构参数,使模型形态随树龄增长而变化。在自主研发的ParaTree系统上,扩展杉木动态生长模拟模块,并以福建省漳平五一林场的二类调查数据为例,动态模拟了杉木的生长过程。模拟结果表明,本方法可直观表达林分中林木个体平均生长状况和大体形态结构特征。树木三维模型描述参数与传统树木统计生长模型结合,有利于重用林业领域淀积大量的生长模型。     

多层递阶回归分析

在经典多层递阶预报模型中,预报因子对模型的贡献大小主要取决于各因子间的相对量值差异,而与预报因子本身的实际预报价值关系甚微。本文针对这一缺陷,提出了一种回归分析与多层递阶方法相结合的统计预报模型——多层递阶回归分析。它既摒弃了经典统计理论中的固定参数预报模型,又较好地消除了由于变量间相对量值差异所造成的贡献差异,充分体现了高相关因子在预报模型中的重要作用。实例计算结果表明,多层递阶回归分析方法的预报效果明显优于经典多层递阶方法和回归分析。     

黄土土性的空间自相关性研究

运用土层的随机场模型研究了绛帐、潼关和延安黄土主要物理力学指标沿深度的统计规律,计算出黄土土性的自相关距离,探讨了黄土土性的空间变异性,结果认为:黄土的物理力学指标沿深度均是统计非均匀的,在实际工程的设计中,如果把土层看成统计均匀,其土性参数的标准差会比实际情况大,从而会导致较大的误差。     

基于径向基函数神经网络的地震液化侧移预测

在已有的地震液化侧移数据库中增加累积绝对速度（CAV₅）这一地震参数,以考虑震源机制对液化侧移的影响。然后采用径向基函数神经网络（RBFNN）方法建立地震液化侧移预测模型,并与其他模型进行对比分析。结果表明,本文模型预测精确度最高;CAV₅在液化侧移预测方面可以代替震级、震中距2项参数;所有参数中,震级、震中距、可液化土层厚度敏感性较高,对液化侧移影响程度较大。     

基于随机森林方法的岩石节理粗糙度系数研究

岩石节理粗糙度系数（ＪＲＣ）是研究岩石力学的重要参数之一。为 精 确 有 效 地 描 述ＪＲＣ,提 出 了 一 种 基 于 随 机 森 林（Ｒａｎｄｏｍｆｏｒｅｓｔ,ＲＦ）算法研究ＪＲＣ 的新方法。首先,详细叙述了 ＲＦ算法的原理和实现流程;然后,简要分析了影响ＪＲＣ的一些统计参数,确定了７个重要的基本变量,岩石节理粗糙度系数（ＪＲＣ）是研究岩石力学的重要参数之一。为 精 确 有 效 地 描 述ＪＲＣ,提 出 了 一 种 基 于 随 机 森 林（Ｒａｎｄｏｍｆｏｒｅｓｔ,ＲＦ）算法研究ＪＲＣ 的新方法。首先,详细叙述了 ＲＦ算法的原理和实现流程;然后,简要分析了影响ＪＲＣ的一些统计参数,确定了７个重要的基本变量,用６组实测数据对训练后的 ＲＦ回归模型进 行 了 测 试,试 验 结 果 表 明:①利用ＲＦ回归模型预计的Ｄ 值、ＪＲＣ 值与实测值的最大相对误差仅为３．８４４％、４．５５３％。②ＲＦ回归模型具有较强的泛化能力,需要考虑的模型参数少,预测精度高,为今后继续研究Ｄ 值和ＪＲＣ 值提供了一种新思路。      

极地海冰-海洋参数遥感反演模型分布式共享研究

本文以SOA开放式架构与OGC标准规范,提出了极地海冰-海洋参数遥感反演模型分布式共享服务体系。服务体系以"模型服务"为核心,探讨了模型服务接口和模型服务的互操作问题。为了简化极地海冰-海洋参数遥感反演模型的分布式共享过程,提出了极地海冰-海洋参数遥感反演模型共享服务平台的概念。共享服务平台处于模型与模型应用客户端之间,可以实现两者之间的数据转化和功能协同,以及实现模型算法与其他功能的分离,使模型开发者可以专注于模型算法的设计和实现。最后,以海冰密集度遥感反演模型和冰间湖识别模型为例,实现了极地海冰-海洋参数遥感反演模型分布式共享方法。     

马氏珠母贝壳长生长模型研究

以Cp统计量作为确定马氏珠母贝壳长最优生长模型的准则,通过Levenberg-Marquardt迭代法求出模型中各生长参数,用Brody、von Bertalanffy、Gompertz、Logistic和Richards 5个生长模型对马氏珠母贝壳长的生长进行了拟合。结果表明,在马氏珠母贝生长的第一年中,壳长生长过程遵循Brody生长模型,其壳长生长极限值为56.572 mm(43.807～69.337,95%置信区间)。     

随机森林算法在机载LiDAR数据林分平均树高 估算中的应用研究

采用机载LiDAR数据估算森林结构参数是当前林业遥感中的研究热点。本文以福建省长汀县朱溪河流域为示范区,探讨了随机森林算法（RF）在机载LiDAR数据林分平均树高估测中的适用性。首先,通过渐进三角网（TIN）算法进行点云滤波并获取相应林分样地的植被点云子集和高程归一化的植被点云;然后,从归一化后的植被点云提取出高度分位数变量以及点云统计特征值等24个变量参数;最后,根据提取的变量参数和野外实测林分均高数据建立研究区林分平均高随机森林回归估测模型。研究结果表明,模型估测的样地平均树高与实测值具有明显线性相关关系,线性回归系数为0.938,相关系数达到0.968。对样地的估测精度都在86%以上,总体平均精度达到了93.17%。研究认为,基于植被点云变量参数的随机森林模型估测林分平均树高具有较高的可靠性。     

高精度曲面建模的中国气候降尺度模型

 与站点统计降尺度插值和动力降尺度相比,高精度曲面建模(HASM)降尺度,具有不需大尺度预报因子,直接从GCM结果构建区域上高空间分辨率的未来气候模拟曲面的优势。HASM降尺度将未来气候,分为历史观测拟合的气候基准值和GCM未来气候变化值进行模拟,精度明显高于传统方法,但常系数全局拟合的气候基准值忽略了降水分布的空间非平稳性,导致降水模拟受到较大影响。为增强降水降尺度的气候背景值的描述能力,通过分析全国尺度降水的非线性非平稳性特点,提出耦合空间变系数气候基准值的HASM空间变系数降尺度模型(HASM-SVDM)以改进HASM对非平稳要素的降尺度能力,并以1961-2010年全国气温降水观测数据结合地形特征信息,利用HASM降尺度方法对HadCM3的A1Fi、A2a和B2a 3种情景的1961-1990、2010-2039、2040-2069和2070-2099时段的全国未来气温与降水进行降尺度模拟。分析表明,耦合全局线性模型的HASM常系数降尺度模型适合全国气温的降尺度模拟,而耦合空间变系数拟合的HASM-SVDM增强了空间非平稳背景值的描述能力,模拟的空间分布更能体现降水总体的非均匀分布趋势,适合全国降水的降尺度模拟。     

南京市住宅价格影响因素分析

住宅价格影响因素的研究对于购房者购买住房及政府制定相关政策具有重要影响。然而,目前对于住宅价格问题的研究大多从宏观角度进行,而从微观角度入手的相对较少。此外,对住宅价格的研究通常需要建立多元回归方程,但这样会造成多重共线性问题,导致伪回归,不能准确地分析各因素对住宅价格的影响。针对该问题,本文从微观角度出发,运用定量与定性相结合的方法,建立特征价格模型,并且选择逐步回归法对其进行修正,将其中显著性不强、经济意义不明显的变量逐步剔除,从而更准确地探究影响住宅价格的主要因素。本文以南京市住宅价格为实验数据,并初步选择12个影响因素对该方法进行验证。结果表明,该方法能够有效地剔除二级商业中心、建筑类型、生活配套设施3个显著性不强的影响因素,保留重点学校等9个影响较大的因素。本文方法更为精准地分析了住宅价格的影响因素,为购房者购买住房及政府制定相关政策提供了一定的理论基础。     

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卡尔曼滤波模型误差的识别

本文在简述卡尔曼滤波模型基础上讨论了模型误差的检验和估计问题,提出了检验卡尔曼滤波模型误差显著性的几种统计假设检验方法,探讨并给出了估计模型误差的方法和公式。     

Feasibility of using reflectance spectroscopy for the analysis of bio-element concentrations in Antarctic ornithogenic sediments

Compared with traditional chemical analysis methods, reflectance spectroscopy has the advantages of speed, minimal or no sample preparation, non-destruction, and low cost. The present study explored the application of the reflectance spectroscopy within near ultraviolet-visible-near infrared region to predict bio-element compositions in the ornithogenic sediments from the maritime Antarctic. A total of 106 samples were taken from four ornithogenic sediment cores on the Ardley Island of Antarctica, 68 samples were used for building calibration equation, and 38 for prediction of nine bio-elements including P, Ca, Cu, Zn, Se, Sr, Ba, F and S. Three multivariate statistical analysis techniques, including stepwise multiple linear regression (Stepwise-MLR), principal component regression (PCR) and partial least squares regression (PLS) were used to develop mathematical relationships between the spectral data and the chemical reference data. The results showed that the regression models constructed by PCR and PLS models have no significant differences, and obviously supervisor to Stepwise-MLR. The correlations between spectra-predicted and chemically analyzed concentrations of nine bio-elements are statistically significant, and the concentration-versus-depth profiles predicted from reflectance spectra using PLS calibration model are consistent with those from actual chemical analysis. These results demonstrated the feasibility of using reflectance spectroscopy to infer bio-element concentrations in the ornithogenic sediments, and thus it is suggested that the reflectance spectroscopy could provide a rapid and valuable technique to indirectly identify whether the sediments were influenced by penguin droppings in the Antarctic region.     

Spatial downscaling of climate variables using three statistical methods in Central Iran

Spatial downscaling methods are widely used for the production of bioclimatic variables(e.g. temperature and precipitation) in studies related to species ecological niche and drainage basin management and planning. This study applied three different statistical methods, i.e. the moving window regression(MWR), nonparametric multiplicative regression(NPMR), and generalized linear model(GLM), to downscale the annual mean temperature(Bio1) and annual precipitation(Bio12) in central Iran from coarse scale(1 km × 1 km) to fine scale(250 m ×250 m). Elevation, aspect, distance from sea and normalized difference vegetation index(NDVI) were used as covariates to create downscaled bioclimatic variables. Model assessment was performed by comparing model outcomes with observational data from weather stations. Coefficients of determination(R2), bias, and root-mean-square error(RMSE) were used to evaluate models and covariates. The elevation could effectively justify the changes in bioclimatic factors related to temperature and precipitation. Allthree models could downscale the mean annual temperature data with similar R2, RMSE, and bias values. The MWR had the best performance and highest accuracy in downscaling annual precipitation(R2=0.70; RMSE=123.44). In general, the two nonparametric models, i.e. MWR and NPMR, can be reliably used for the downscaling of bioclimatic variables which have wide applications in species distribution modeling.     

半参数模型核光滑估计与模拟分析

讨论了半参数模型核光滑估计的两种方法．即偏核光滑估计与偏残差估计，通过构造核估计广义交叉核实函数自动选取窗口参数。最后将模拟计算与光滑样条进行了比较，表明该方法可以用于曲线拟合或模型系统误差估计。     

Study on mine earthquake activity rules for Laohutai coal mine in Fushun of Liaoning

According to earthquake data of Fushun earthquake administration,the seismic analysis and statistical methods are utilized in order to analyze earthquake frequency,"b"-value timing and energy creep trends in Laohutai coal mine. By using least squares linear regression method,the relational expression between frequency and magnitude of mine earthquake in Laohutai coal mine is given. And the possible largest magnitude mine earthquake inferred has also been calculated. And this paper also provides a theoretical basis for further study of mine earthquake activity.     

A multiobjective evolutionary optimization method based critical rainfall thresholds for debris flows initiation

At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression.With relatively weak fault tolerance,this method not only ignores nonlinear effects but also is susceptible to singular noise samples,which makes it difficult to characterize the true quantization relationship of the rainfall threshold.Besides,the early warning threshold determined by statistical parameters is susceptible to negative samples(samples where no debris flow has occurred),which leads to uncertainty in the reliability of the early warning results by the regression curve.To overcome the above limitations,this study develops a data-driven multiobjective evolutionary optimization method that combines an artificial neural network(ANN) and a multiobjective evolutionary optimization implemented by particle swarm optimization(PSO).Firstly,the Pareto optimality method is used to represent the nonlinear and conflicting critical thresholds for the rainfall intensity I and the rainfall duration D.An ANN is used to construct a dual-target(dual-task) predictive surrogate model,and then a PSO-based multiobjective evolutionary optimization algorithm is applied to train the ANN and stochastically search the trained ANN for obtaining the Pareto front of the I-D surrogate prediction model,which is intended to overcome the limitations of the existing linear regression-based threshold methods.Finally,a double early warning curve model that can effectively control the false alarm rate and negative alarm rate of hazard warnings are proposed based on the decision space and target space maps.This study provides theoretical guidance for the early warning and forecasting of debris flows and has strong applicability.     

Study on mine earthquake activity rules for Laohutai coal mine in Fushun of Liaoning

According to earthquake data of Fushun earthquake administration,the seismic analysis and statistical methods are utilized in order to analyze earthquake frequency,b-value timing and energy creep trends in Laohutai coal mine. By using least squares linear regression method,the relational expression between frequency and magnitude of mine earthquake in Laohutai coal mine is given. And the possible largest magnitude mine earthquake inferred has also been calculated. And this paper also provides a theoretical ...