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。     

BDS星载原子钟周期项特性及预报研究

采用频谱分析方法对BDS星载原子钟的周期项变化规律进行分析,继而构建了附有周期项的精密钟差预报模型。在此基础上,对预报模型的短期预报效果和不同类型卫星的预报精度差异进行分析。结果表明,不同类型的卫星均表现出较为显著的12 h和24 h的周期项变化规律,但其对应的能量幅值存在差异;与传统的二次多项式预报模型相比,结合周期项改正的钟差预报模型能够提高预报精度,6 h的预报精度约为2 ns,24 h的预报精度约为10 ns。     

基于小波分解与GA-LSSVM的GPS可降水量短临预报

针对GPS可降水量时间序列具有非线性、非平稳性的特征,研究一种基于小波分解（WD）、遗传算法（GA）和最小二乘支持向量机（LSSVM）的GPS可降水量短临预报方法。先采用小波分解将GPS可降水量时间序列分解成便于预报的低频分量和高频分量;然后利用遗传算法优化LSSVM参数,进而对各分量建立预报模型;再将各分量预报结果进行叠加重构得到最终预报结果。选取两组数据进行实验,并将预报结果分别与LSSVM和遗传小波神经网络（GA-WNN）预报结果进行对比。结果表明,该组合模型具有良好的泛化能力,可有效解决神经网络易陷于局部极小的问题,提高了全局预报精度。     

完全规格化缔合勒让德函数常用递推算法的适用性

完全规格化缔合勒让德函数递推算法的适用性是衡量算法优劣的重要标志。从第一相对数值精度、第二相对数值精度和计算速度等方面对4种常用的递推算法--标准向前列递推算法、标准向前行递推算法、跨阶次递推算法和Belikov递推算法的适用性进行分析。结果表明,标准向前行递推算法适用范围最小;对于cosθ∈[－1,1],在1 900阶内,标准向前列递推算法、跨阶次递推算法和Belikov递推算法均适用,且第1种算法速度最快;在3 000阶内,跨阶次递推算法和Belikov递推算法适用,且后者更优。     

基于EEMD-样本熵和GM-SVM的钟差预报方法研究

提出一种基于集合经验模态分解、样本熵和灰色支持向量机相结合的卫星钟差预报方法。首先利用EEMD对钟差序列进行分解,然后采用样本熵对复杂度相似的分量进行叠加组合,进而对各新分量建立高频支持向量机和低频灰色GM(1,1)进行滚动式预报,最后叠加各预报结果得到钟差预报值。用IGS提供的钟差数据作实验,与灰色模型、支持向量机、遗传小波神经网络的多步预报进行对比分析。结果表明,该方法预报精度较高,对于较长时间的钟差预报也能保证较好的预报效果。     

一种顾及钟差周期误差和随机特性的卫星钟差预报方法

提出一种顾及钟差周期误差和随机特性的卫星钟差预报方法。首先通过比较二次多项式加1、2、3、4个主要周期误差的模型,取其优者求得钟差预报的拟合值;然后针对拟合残差值的随机特性采用灰色模型进行建模,求得拟合值残差预报值;最后,将其与之前求得的预报值相结合得到最终的钟差预报值。采用IGS的15 min精密钟差数据进行实验,结果表明,在短期预报中,加2个主要周期误差的模型预报性能最好,并且新模型的预报精度优于常用算法。     

地球极移参数高精度双差分LS+AR预报方法研究

提出基于双差分最小二乘LS+AR模型的高精度极移参数预报方法。首先,对极移数据进行双差分处理,用以增强数据平稳性,获取差分极移数据,并采用LS方法对差分极移数据进行拟合,获取极移残差数据;其次,利用AR模型对极移残差数据进行预报;然后,综合LS外推预报值与AR模型预报值获取差分极移预报值;最后,对差分极移预报结果进行逆双差分处理,获取高精度的极移预报值。将该方法应用于实际极移参数预报中,结果表明,1d的极移X分量(PMX)预报精度优于0.25mas,极移Y分量(PMY)预报精度优于0.2mas。将该预报结果与国际EOP_PCC预报结果对比表明,极移短期预报精度与EOP_PCC预报结果相当,1d的预报精度略优于EOP_PCC预报结果。     

对流参数主分量旋转法在西昌地区雷暴预报中的应用

为研究对流参数主分量旋转方案对西昌地区雷暴预报的影响,利用2006~2008年雨季(6~9月)T213模式输出产品,采用F-分值法和主分量旋转法,分别初选和精选因子,并结合二元Logistic回归分析,建立了西昌北郊某站的雷暴释用预报的模型,并对该站2010年雨季雷暴进行预报试验。试验结果表明:精选因子的KMO检验值在0.65~0.85,且Bartlett球形度检验显著,说明该站的雷暴对流参数适用于因子分析;极大方差旋转能够较好地分离因子载荷,对雷暴信息的提取能力较强;试报的TS评分在0.3左右,24小时预报的Hedike评分可达0.337,模型的预报准确率较高,持续性较好。综上所述,采用主分量旋转法对雷暴的MOS预报有一定的改善效果。     

GM(1,1)模型初始条件优化及其在GNSS钟差预报中的应用

针对现有改进GM(1,1)模型在导航卫星钟差预报中性能无明显提升的问题,提出通过优化初始条件来提高钟差预报精度的方法。首先构建初始条件未知的GM(1,1)预报模型,然后采用原始序列的最新分量求解初始条件,最后利用该模型对IGS提供的精密钟差数据进行预报实验。结果表明,将初始条件优化后的GM(1,1)模型用于钟差预报切实可行,且预报精度比传统GM(1,1)模型有较大提高。     

基于混沌加权一阶局域法的IGS超快星历钟差预报

为提高IGS超快星历钟差预报产品的精度,针对卫星钟差数据具有确定项和随机项成分的特点,在采用多项式模型对钟差确定项建模的基础上,提出采用混沌加权一阶局域法对钟差随机项进行建模预报。仿真结果表明,采用混沌加权一阶局域法的预报精度优于超快星历钟差预报产品。     

The application of a Grey Markov Model to forecasting annual maximum water levels at hydrological stations

Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years’ data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.     

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基于GNSS技术的大型建筑物实时动态监测与模型化

对高楼的GNSS连续监测单历元动态解算发现,在正常情况下单日解解算结果的位移变化具有一定规律。采用频谱分析提取位移变化主成分,利用高阶谐函数构建高楼实时监测位移预报模型。以同济大厦监测为例,所构建的预报模型预报结果与实际位移值相吻合,水平方向RMS优于3 mm,垂直方向RMS优于7 mm。     

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基于GIS评价活动断裂对公路的影响——以宁夏地区为例

为了查清活动断裂对公路的影响,总结了宁夏地区9条全新世活动断裂的基本特征和活动性,确定了评价和预测活动断裂对公路工程影响的指标体系,并建立了分级标准。采用优先排序法对指标进行筛选和优化,运用主成分分析法确定权重,选用多级模糊综合评判法建立了评价模型。以ARCGIS9为平台,建立了宁夏地区活动断裂空间数据库,运用矢量叠加运算实现了宁夏地区活动断裂对公路影响的现状评价,运用栅格叠加运算实现了宁夏地区海原活动断裂带对公路影响的预测评价。预测结果表明,宁夏地区对公路影响大的海原活动断裂面积为12.34 km2,占全区总面积的1.0%;影响较大的为271.28 km2,占总面积的21.4%;影响中等的为400.05 km2,占总面积的31.6%;影响小的为585.04 km2,占总面积的46.0%。     

基于Theil不等系数的IOWA算子最优变形组合预测模型

针对传统组合预测模型中各单项预测模型赋权问题的不足,提出一种基于Theil不等系数的IOWA算子最优变形组合预测模型。利用IOWA算子,依据各单项预测模型预测值各时间点精度的不同,赋予不同的权系数,建立最优组合预测模型,并将其应用于变形数据分析与预测。实验结果表明,本文方法的预测结果比各单项模型与传统组合模型的结果更准确。     

Nash model parameter uncertainty analysis by AM-MCMC based on BFS and probabilistic flood forecasting

A hydrologic model consists of several parameters which are usually calibrated based on observed hydrologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simulated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.     

Common offset ground penetrating radar data inversion based on ray theory

The authors use the common offset ground penetrating radar(GPR) data inversion based on ray theory to estimate interval velocity and to obtain the relative permittivity. In the ray-tracing based inversion, the input data are the offset distance between antennas, the velocity of the first layer, the pick-up amplitude and re-ference amplitude of each reflection layer. The thickness and velocity of each layer are calculated by this recursive method. Firstly, the horizontal homogeneous layered medium model is established, and the ideal inversion results are obtained. Subsequently, Monte Carlo method is used to establish a randomly undulating homogeneous layered medium model. The common offset GPR data for the built geological model is then simulated by finite-difference time-domain(FDTD). It proved that this ray-tracing based inversion method is feasible for the horizontal layered geological model, even the layered geological model with random undulation. Undulation, represented by RMS height and CL(correlation length), influences the inversion results. Finally, a more complex geological model--pinch-out model was established. In the pinch-out model, the pinch-out interface can be clearly identified, though there is a false anomaly, which will not significantly affect the identification of the underground medium structure.