基于支持向量机的围岩定性智能分级研究

本文将数据挖掘的新方法支持向量机应用于隧道围岩分级.支持向量机是一种基于统计学习理论的新的学习算法,比神经网络算法能更好地解决小样本问题.选用岩层厚度、岩体结构、嵌合程度、风化程度、地下水特征、节理发育程度、榔头敲击声和地应力等 8 个定性指标作为评判因子,用泥巴山隧道采集的实际数据作为样本对不同核函数的支持向量机进行...     

基于遗传算法-支持向量机模型在热带气旋强度预报中的应用

利用遗传算法对支持向量机(SVM)模型参数进行寻优,找到最优参数组合后代入SVM模型中,得到基于遗传算法的支持向量机模型(GA-SVM),利用此模型对热带气旋强度进行预报实验。该模型对热带气旋强度12 h、24 h和48 h的预报平均绝对误差分别为3.01 m/s、4.46 m/s和6.57 m/s;比最小二乘回归法的预报精度分别提高了12%、11%、14%。     

Multi-scale support vector algorithms for hot spot detection and modelling

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.     

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

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.     

广东海岸带可持续发展决策支持系统总体框架

在阐述了广东海岸带可持续发展决策支持系统的概念后,根据系统的应用需求,确立了系统的总体目标和功能结构,并重点研究了地理信息技术、空间元数据、空间数据仓库技术和决策支持模型与方法等关键技术,在广东海岸带复杂动态海量多源数据的获取、更新、管理、分析与决策中的重要意义和作用,力求为广东海岸带可持续发展决策支持系统的详细分析设计和开发实践提供规范化的解决方案。     

应用SVM方法进行沉积微相识别

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     

基于模式识别理论的卡钻类型判别分析

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。     

Sensitivity analysis of support safety factor for predicting the effects of contributing parameters on roof falls in underground coal mines

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.     

南京地区冬季路面结冰天气标准及其预测

本文通过对南京地区1984—2003年20 a 110个降水结冰样本当日温度的统计分析,讨论了南京地区结冰时间变化和各影响温度的变化规律,总结了对结冰预报具有指示意义的关键因子;同时利用支持向量机方法探讨了南京地区结冰预报方法,该方法具有显著的预报价值。在此基础上根据Norrman提出的路面打滑分类,结合南京地区具体情况得出了南京雨雪天气路面结冰的类别、标准和预测预报方法。