共查询到17条相似文献,搜索用时 828 毫秒
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改进的灰色预测模型在地面沉降预测中的应用 总被引:7,自引:0,他引:7
在地面沉降这一复杂系统中,既含有已知的又古有未知的或非确定的信息,可以作为一个灰色系统来研究.本文针对地面沉降的下沉曲线非线性特征,提出用一种基于残差灰色预测模型对地面沉降量时间序列进行研究.结果表明,通过改进后的灰色预测模型,预测精度得到了提高,在沉降量比较大和水准点比较稀少的地区,利用此模型预测地面沉降可减少地面沉降监测经费,并能实时提供地面沉降预警信息. 相似文献
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线性回归模型在北京平原地面沉降预测中的应用 总被引:2,自引:0,他引:2
北京平原的地面沉降日趋严重,地面沉降问题已经成为北京平原区最主要的地质灾害。本文在一系列地下水开采量、地下水水位和地面沉降量实测数据的基础上,运用Excel软件建立了地下水开采量与沉降量、地下水位与沉降量之间的线性回归方程。基于地面沉降量和地下水开采量或地下水位之间的回归方程对地面沉降量进行预测,并对预测值的可靠程度进行了验证。本文利用所建立的线性回归模型对地面沉降量进行预测,可以为地面沉降的控制提供依据。 相似文献
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徐州大屯中心区地面沉降趋势预测 总被引:1,自引:0,他引:1
徐州大屯中心区1988年建立了地面沉降观测系统,2005年最大累计沉降量达到600 mm.累计沉降量大于100 mm的地区面积达到11.57 km2.本文根据近20年的沉降观测数据分析了中心区地面沉降的时空分布特征,并采用灰色模型方法,对地面沉降趋势进行了预测,结果表明到2010年最大累计沉降量将达到753 mm,累计沉降量大于100 mm的地区将达到32.86 km2,对中心区的建筑、地下管网将造成较大威胁,应尽快采取防治措施. 相似文献
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通过对太原市地面沉降资料、气象水文和地质条件以及地下水等大量资料的分析与整理,考虑到太原市各个沉降中心的沉降趋势不尽相同,对太原市吴家堡、西张、万柏林和下元4个沉降中心分别建立BP神经网络模型,并基于训练好的BP神经网络模型,在太原市地下水开采量的规划方案下,预测了在不同降水保证率下2009-2015年地面沉降的趋势,... 相似文献
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岩溶水系统的径向基神经网络仿真 总被引:1,自引:0,他引:1
岩溶水系统的复杂性决定了其输入与输出间具有非常复杂的非线性关系,利用人工神经网络方法进行系统的仿真是一种十分有效的手段。本文以MATLAB为平台,介绍了RBF网络的基本原理与训练方法,具有结构自适应确定、输出不依赖初始权值的优良特性。试用该方法建立了济南市岩溶水系统地下水位及其影响因子间的RBF网络模型,讨论了训练样本集与检测样本集的构建、原始数据的预处理方法、神经网络训练误差设置等重要环节,并与同结构的BP网络进行了对比,其结果BP网络效果依赖初始权值,表现出极不稳定性,且训练速度更慢,RBF网络具有更好的应用价值。 相似文献
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Prediction of ground subsidence in Samcheok City,Korea using artificial neural networks and GIS 总被引:4,自引:0,他引:4
This study shows the construction of a hazard map for presumptive ground subsidence around abandoned underground coal mines
(AUCMs) at Samcheok City in Korea using an artificial neural network, with a geographic information system (GIS). To evaluate
the factors governing ground subsidence, an image database was constructed from a topographical map, geological map, mining
tunnel map, global positioning system (GPS) data, land use map, digital elevation model (DEM) data, and borehole data. An
attribute database was also constructed by employing field investigations and reinforcement working reports for the existing
ground subsidence areas at the study site. Seven major factors controlling ground subsidence were determined from the probability
analysis of the existing ground subsidence area. Depth of drift from the mining tunnel map, DEM and slope gradient obtained
from the topographical map, groundwater level and permeability from borehole data, geology and land use. These factors were
employed by with artificial neural networks to analyze ground subsidence hazard. Each factor’s weight was determined by the
back-propagation training method. Then the ground subsidence hazard indices were calculated using the trained back-propagation
weights, and the ground subsidence hazard map was created by GIS. Ground subsidence locations were used to verify results
of the ground subsidence hazard map and the verification results showed 96.06% accuracy. The verification results exhibited
sufficient agreement between the presumptive hazard map and the existing data on ground subsidence area.
An erratum to this article can be found at 相似文献
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岩土参数的随机性会直接影响边坡稳定性评价结果的精度。首先,依据边坡参数的常用分布特征,利用拉丁超立方抽样法生成若干组边坡土性参数和几何参数的随机样本,用有限元强度折减法求解各组样本对应的边坡安全系数。再考虑土性参数的空间变异性,在二维随机场模型下将蒙特卡罗模拟和有限元强度折减法相结合求解各组样本对应的边坡失效概率。然后,利用样本数据及其安全系数和失效概率对径向基函数(RBF)神经网络进行训练和测试,从而建立边坡安全系数和失效概率的预测模型。算例表明,二维随机场模型能相对精确地考虑参数的空间变异性;在此基础上建立的神经网络模型对边坡的安全系数和失效概率具有较高的预测精度,且能极大地节省边坡稳定性分析的时间。 相似文献
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Mohammad Zare Hamid Reza Pourghasemi Mahdi Vafakhah Biswajeet Pradhan 《Arabian Journal of Geosciences》2013,6(8):2873-2888
Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning. 相似文献
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针对沈阳地铁一号线重工街站至启工街站区间隧道开挖引发地面沉降变形的问题,利用现场实测的地表沉降变形数据建立BP神经网络模型,并进行网络训练与预测。预测结果表明,时间序列神经网络模型能够很好地表达地面沉降监测数据序列间的非线性关系。利用BP神经网络建立的预测模型,所得预测值与实测值拟合很好,是预测地铁施工引发地面沉降变形的一种有效方法,能为沈阳地铁隧道的设计及施工提供科学合理的依据。 相似文献
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Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model. 相似文献