平稳自回归模型在高层建筑沉降预测中的应用

首先介绍了平稳自回归模型-AR(p)模型,然后将该模型应用到高层建筑沉降预测实例中,通过沉降实测数据与预测数据进行对比分析,其结果均满足精度要求,表明:AR(p)模型在高层建筑的沉降预测中具有很好的适用性。     

基于GM(1,1)等维新息模型的矿山沉降预测

介绍了GM(1,1)灰色模型的建立过程及模型的精度评定方法,采用等维新息模型对某矿工业广场的沉降趋势进行了预测,并用残差序列建立GM(1,1)模型进行修正,通过与实测的结果对比表明,模型的预测具有较高的精度,模型可靠合理。     

基于时间序列分析和灰色理论的建筑物沉降预测模型研究

基于时间序列分析方法建立建筑物沉降预测模型,其中通过二次移动平均法提取出沉降监测序列中的趋势项,并在此基础上采用灰色系统理论动态GM（1,1）模型进行趋势项预测。实际算例结果表明,该模型能够较好地预测沉降变化的发展趋势,并具有较高的预测精度,证明了该预测模型具有一定的可行性和有效性。     

利用MorPAS对上黑龙江成矿带进行金成矿预测

上黑龙江成矿带主要由早-中侏罗世河流-湖泊-洪积相含煤碎屑岩组成,目前已发现多个中小型矿床,在该区进行成矿预测意义重大。文章利用MORPAS成矿预测系统,采用证据权重法进行了成矿预测工作,在充分研究区内已知矿床的成矿规律基础上,以1：257Y地质图为背景,物化探异常分析子系统对1：20万区域化探数据进行C—A分形确定异常下限,综合现有的找矿标志,提取有利的成矿要素,进行针对处理,制成上黑龙江成矿带MORPAS金成矿预测分析图,提供成矿靶区,为森林高覆盖区提供找矿有利单元。     

Downscaling in remote sensing

Downscaling has an important role to play in remote sensing. It allows prediction at a finer spatial resolution than that of the input imagery, based on either (i) assumptions or prior knowledge about the character of the target spatial variation coupled with spatial optimisation, (ii) spatial prediction through interpolation or (iii) direct information on the relation between spatial resolutions in the form of a regression model. Two classes of goal can be distinguished based on whether continua are predicted (through downscaling or area-to-point prediction) or categories are predicted (super-resolution mapping), in both cases from continuous input data. This paper reviews a range of techniques for both goals, focusing on area-to-point kriging and downscaling cokriging in the former case and spatial optimisation techniques and multiple point geostatistics in the latter case. Several issues are discussed including the information content of training data, including training images, the need for model-based uncertainty information to accompany downscaling predictions, and the fundamental limits on the representativeness of downscaling predictions. The paper ends with a look towards the grand challenge of downscaling in the context of time-series image stacks. The challenge here is to use all the available information to produce a downscaled series of images that is coherent between images and, thus, which helps to distinguish real changes (signal) from noise.     

利用LS_AR模型对极移参数的中长期预报

为了满足深空探测器自主导航定位对极移参数中长期预报的需求,阐述了基于LS_AR模型的极移参数中长期预报和精度评定的原理,提出了4种改进方案对LS_AR模型的构建进行优化,并利用IERS提供的1990～1996年的极移参数的时间序列检验4种优化方案,得到了最优的预报模型,在400 d跨度上,其预报结果的平均绝对误差比未优化的模型小3 mas左右。     

Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index

In the present study, prediction of agricultural drought has been addressed through prediction of agricultural yield using a model based on NDVI-SPI. It has been observed that the meteorological drought index SPI with different timescale is correlated with NDVI at different lag. Also NDVI of current fortnight is correlated with NDVI of previous lags. Based on the correlation coefficients, the Multiple Regression Model was developed to predict NDVI. The NDVI of current fortnight was found highly correlated with SPI of previous fortnight in semi-arid and transitional zones. The correlation between NDVI and crop yield was observed highest in first fortnight of August. The RMSE of predicted yield in drought year was found to be about 17.07 kg/ha which was about 6.02 per cent of average yield. In normal year, it was 24 kg/Ha denoting about 2.1 per cent of average yield.     

Best prediction in linear models with mixed integer/real unknowns: theory and application

In this contribution, we extend the existing theory of minimum mean squared error prediction (best prediction). This extention is motivated by the desire to be able to deal with models in which the parameter vectors have real-valued and/or integer-valued entries. New classes of predictors are introduced, based on the principle of equivariance. Equivariant prediction is developed for the real-parameter case, the integer-parameter case, and for the mixed integer/real case. The best predictors within these classes are identified, and they are shown to have a better performance than best linear (unbiased) prediction. This holds true for the mean squared error performance, as well as for the error variance performance. We show that, in the context of linear model prediction, best predictors and best estimators come in pairs. We take advantage of this property by also identifying the corresponding best estimators. All of the best equivariant estimators are shown to have a better precision than the best linear unbiased estimator. Although no restrictions are placed on the probability distributions of the random vectors, the Gaussian case is derived separately. The best predictors are also compared with least-squares predictors, in particular with the integer-based least-squares predictor introduced in Teunissen (J Geodesy, in press, 2006).     

大桥施工过程中的沉降观测及沉降灰色模型预测的探索

在桥梁施工过程中经常需要对桥梁墩台进行沉降观测。本文结合木瓜溪大桥墩台施工期间的多次沉降观测,讨论了桥墩台施工检测过程中控制网布设及其沉降规律,为较大型桥梁的施工提供较为科学的沉降规律。最后,用灰色系统模型对沉降监测点预测进行了探索,得出了一些有用的结论。     

青海省主体功能区规划研究

主体功能区规划是优化国土开发格局,推动区域协调发展的一项重大战略决策。根据《国务院关于编制主体功能区规划的意见》和国家《省级主体功能区划分技术规程》的总体要求,利用遥感、地理信息系统等空间分析技术和手段,以及专家咨询等方法,对全省的国土空间进行综合分析评价,提出主体功能区试划方案,进而为编制全省主体功能区规划奠定科学基础。