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.     

基于遥感影像的土地动态变化监测

土地利用/覆盖变化是目前研究全球及区域环境的一个重要领域,在城镇化加速的今天,城镇的土地利用格局也发生了飞速的变化。本文通过其一研究区内的Landsat TM遥感影像进行处理,获取了2007~2016年10个时相土地利用/覆盖信息,通过不同的预测模型对监测到的数据进行处理及比较,根据相应的最优预测方法预测了2017~2019年南昌市各土地类型的数据,由此研究并探讨了南昌市土地利用/覆盖的时空格局变化。     

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。     

一种导航卫星中长期轨道预报方法

基于神经网络混合建模的思想提出一种针对导航卫星的中长期轨道预报方法,在原动力学模型的基础上引入神经网络模型作为补偿,从而获得新的预报模型。在训练过程中神经网络通过学习动力学模型轨道预报误差来掌握其变化规律,并在预报过程中为动力学模型预报提供补偿,以提高预报精度。对GPS卫星动力学模型中长期预报误差的特点进行分析,然后根据所得结论提出混合模型的中长期(15 d以上)预报方案,最后通过对GPS卫星的仿真试验证明混合模型的改进效果,结果表明新方法在15~40 d的预报上表现出很好的改进效果。     

基于单邻点多波段预测的高光谱图像无损压缩算法

提出了一种基于聚类-单邻点、多波段预测-熵编码的高光谱数据无损压缩方法。根据谱向特征,进行高光谱图像矢量聚类。对各个分类,采用单个空间位置邻点、多个波段作为预测数据,训练预测系数,进行三维预测。残差采用Golomb-Rice编码。实验证实了算法的有效性。