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Alexei Pozdnoukhov Mikhail Kanevski 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(5):647-660
The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining
and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming
at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data
model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments
in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector
regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial
scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically
from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the
data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations
at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the
possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early
warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application
to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident. 相似文献
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居民地方感对区域旅游发展支持度影响——以九寨沟旅游核心社区为例 总被引:19,自引:3,他引:16
基于"地方感"、"期望理论"两个基本理论,从旅游核心社区居民地方感、旅游发展期望、旅游发展获益感知、旅游发展成本感知以及旅游发展支持度五个结构变量Ⅲ发,通过引入旅游发展期望值作为中介变量,构建居民地方感对旅游发展支持度内部影响机制的理论模型.以世界遗产地几寨沟为实证研究对象,通过验证性因子分析和三阶段结构方程模型的定量分析,校验理论模型中各结构因子、结构因子与其所含维度之间影响路径及其系数,定量测度了居民的旅游发展期望在居民地方感与旅游发展正反影响感知关联中的中介效用和影响机制,及对旅游发展支持度的潜在影响模式.研究结果表明:①居民地方感对居民旅游发展正反影响感知的作用存在差异,地方感对获益感知的作用远大于成本感知.②居民从旅游发展中感知获益有助于他们对当地旅游发展持更积极态度.而成本感知则会产生消极影响.③居民旅游发展期望中介效用的存在性和有效性,居民地方感通过发展期望,强化了对旅游发展影响感知及旅游支持度作用.④发展期望在居民旅游发展正反影响感知两结构模型中的中介效用存在强弱,在感知获益子结构模型的中介作用强于感知成本子模型. 相似文献
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应用SVM方法进行沉积微相识别 总被引:15,自引:1,他引:14
作者针对目前沉积微相中的特征提取问题,提出了应用SVM(支持向量机)方法进行沉积微相识别的方案。该方法不是象传统方法那样首先试图将原输入空间降维(即特征选择变换),而是设法将输入空间升维,以求在高维空间中问题变得线性可分(或接近线性可分)。因为升维后只是改变了内积运算,并没有使算法复杂性随着维数的增加而增加,因此这种方法才是可行的。所以。利用该方法更能胜任实际情况。实际处理表明该方法在小样本情况下 相似文献
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将模式识别理论中的支持向量机、Bayes判别分析以及多元回归分析应用于卡钻类型判别分析,建立了基于模式识别理论的卡钻判别模型。以近几年川东北卡钻数据为例进行了算例分析,结果表明:采用支持向量机、Bayes判别法及多元回归法对卡钻类型判别的结果与实际结果的误判率分别为1.92%,11.52%,61.54%。支持向量机(SVM)判别结果精度最高,但其判别方程式较为复杂,不能直观看出各分量对结果的影响程度;多元回归分析判别方程形式简单,可以直观表达各参量与卡钻之间的密切程度,但其判别精度较低;Bayes判别法计算精度介于二者之间,但其判别精度与判别式的个数密切相关。 相似文献
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Sensitivity analysis of support safety factor for predicting the effects of contributing parameters on roof falls in underground coal mines 总被引:2,自引:0,他引:2
Roof falls accounted for 18.18% of all fatal accidents in Indian coal mines, contributing about 35.29% of all fatal accidents in below-ground operations in 2005. The support safety factor, always preferred in support planning and design of underground coal mines, may be an important predictor for roof falls. In this paper, geotechnical data were collected from 14 roof fall incident places in an underground coal mine, located in the Eastern India, which has bord and pillar method of workings. The mean value of probabilistic support safety factor for the case study mine was found to be 1.24. However, the probability, of the estimated support safety factor of less than or equal to one, was found to be 0.246. Sensitivity analysis was conducted to analyze the effects of the contributing parameters on support safety factor and the likelihood of the roof fall. The multi-variate regression analysis was carried out for the data generated by Monte Carlo method to correlate the contributing factors to support safety factor. It ranked gallery width as the first parameter to control the support safety factor. 相似文献
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