共查询到20条相似文献，搜索用时 109 毫秒
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针对地铁通风井深基坑工程的沉降数据扰动因子多、传统灰色模型预测效果差的不足,本文以DGM(1,1)模型为基础,利用弱化缓冲算子能够有效地处理含有扰动因素的原始监测数据的优势,较好地改善了基础模型的预测精度。本文在系统分析弱化缓冲算子对DGM(1,1)预测修正过程的基础上,选用3种弱化缓冲算子对风井基坑周围的地表监测点D2和给水管线监测点S2进行沉降量预测的对比分析。工程实例分析表明:平均弱化缓冲算子修正后的DGM(1,1)模型具有较高的预测精度,可以用于工程中的沉降预测。 相似文献
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通过分析建筑物沉降监测数据的特点,采用了指数平滑和曲线拟合模型对建筑物沉降监测数据进行处理,实现了两种数据处理模型的建模过程,并用于建筑物沉降量的实际预测,验证了模型的可行性。 相似文献
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针对某煤矿充填开采地表岩移观测站的建设设计进行研究,以Visual Basic 6.0为开发平台,设计开发了煤矿地表沉降变形数据处理与预警系统,实现了地表沉降监测数据导入、动态管理、处理分析、查询、时间变化曲线图显示等功能,并通过了实例应用,更好地指导了信息化施工。 相似文献
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马玉梅 《测绘与空间地理信息》2016,(12):183-185
高层建筑物的监测周期一般较长,数据量大,数据处理和分析较为复杂,因此,需要选择一种较为合适的、贴近工程实际的分析方法.本文以某高层建筑物的沉降监测数据为例,阐述了多元回归分析模型中影响因子的确定、回归模型的最小二乘参数估计以及回归模型的显著性检验,并比较了沉降累计值的预测值和真实值,结果显示:二者的残差较小,可见预测精度较高,说明多元回归分析在高层建筑沉降监测数据处理中是行之有效的. 相似文献
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结合某大坝工程实测数据,建立该大坝位移量和相关因子的逐步回归模型和神经网络模型,并对两者模型结果进行比较,结果表明神经网络方法在大坝变形分析和预报方面效果良好。 相似文献
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Targeting the multicollinearity problem in dam statistical model and error perturbations resulting from the monitoring process,we built a regularized regression model using Truncated Singular Value Decomposition(TSVD).An earth-rock dam in China is presented and discussed as an example.The analysis consists of three steps:multicollinearity detection,regularization pa-rameter selection,and crack opening modeling and forecasting.Generalized Cross-Validation(GCV) function and L-curve criterion are both adopted in the regularization parameter selection.Partial Least-Squares Regression(PLSR) and stepwise regression are also included for comparison.The result indicates the TSVD can promisingly solve the multicollinearity problem of dam regression models.However,no general rules are available to make a decision when TSVD is superior to stepwise regression and PLSR due to the regularization parameter-choice problem.Both fitting accuracy and coefficients’ reasonability should be considered when evaluating the model reliability. 相似文献
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Xiaoli Wang Xiaoli Zhang Guomin Zhou 《Journal of the Indian Society of Remote Sensing》2017,45(5):785-794
The rice disease is one of the most serious injurious factors that cause major loss of rice production and subsequent economy in agricultural industry. This study explored a new method for obtaining information of the rice disease in a short term through model regression methods. The spectrum characteristics of rice leaves under different disease damage were firstly analyzed for its relationship with rice disease level. The sensitive bands of the spectrum for accurately supervising rice diseases were selected with principal component analysis (PCA). The stepwise regression method and BP neural network were both used to establish the spectrum-based models for recognizing rice diseases. Results showed that five major characteristic bands were determined by PCA (990, 1850, 660, 1921, and 1933 nm) for monitoring foliar rice diseases, among which the edge area for red light had the best correlation with rice disease level was also selected as the parameter to establish the model. Specifically, the composite reflectivity of wavelengths between 990 and 1933 nm was negatively related to rice brown spot diseases stress, which was then used to establish the model. Parameters of the red edge area and the ranged reflectivity between 660 and 990 nm were used to establish models for monitoring rice sheath blight diseases. Totally, there were 60 samples employed to build models for identifying the two diseases by the stepwise regression method and the BP neural network method, and the rest 41 ones were used for further model verification. Compared with the stepwise regression analysis, BP neural network was evaluated to perform better with characteristic bands at 660, 990, and 1933 nm. In conclusion, the establishment of the function model in our study can be implemented to monitor rice diseases, which provided a theoretical basis for indirect and rapid monitoring rice diseases. 相似文献
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为了克服回归模型的一些缺点,将小波分析引入到回归模型里面,建立一种混合模型。本文通过介绍回归模型和小波变换,建立小波回归的混合模型,介绍建模的流程,通过小波变换来优化监测数据,再得到回归模型的估计函数。通过与回归模型的比较分析,说明这种混合模型较优的分析效果,更好的预测精度。 相似文献
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沉降监测的数据分析与预计在工程建设中具有决策辅助作用。选择合理的预测模型进行沉降预计,可以提高预计精度。结合工程实例,对焦作市×××项目8号楼进行沉降监测,分别用回归分析模型和灰色理论GM(1,1)模型进行数据处理,从而说明两种模型各自适用的环境条件和优缺点,预计效果令人满意。 相似文献
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广西赣龙复线铁路工程是我国东南软弱围岩复杂地质隧道代表,课题组成功探索了利用回归分析模型在隧道施工过程中进行安全预报,模型建立在认真进行的前期隧道量控监测大量实际数据基础上,并进行实测数据验证并修改模型参数,最后进行回归分析预报。文章主要从目的、回归分析建立、模型验证、回归分析预报等几方面阐述了隧道在施工中安全预警工作,达到了预测风险的目的,保证了施工人员生命安全,避免了国家财产损失。 相似文献