基于支持向量机的喀斯特山区土壤环境质量评价——以贵州北部一茶叶园区为例

以贵州北部一茶叶园区80个表层土壤样品为研究对象,对其Hg、As、Cd、Pb、Cr和Cu含量进行测定,在MATLAB中应用支持向量机构建土壤环境质量评价模型,并与模糊综合评价法和内梅罗综合污染指数法的评价结果对比分析,探究支持向量机模型在喀斯特山区土壤环境质量评价中的适用性,其结果表明:研究区土壤质量Ⅰ类与Ⅱ类样品比例为33∶7,土壤环境质量大多数为I类;支持向量机方法的评价结果与模糊综合评价法和内梅罗综合污染指数法结果的相同率分别达到82.5%和80.0%,并分析结果有差异的样品,发现支持向量机评价结果更符合实际情况,这说明该模型适用于土壤环境质量的评价。      

泥石流平均流速预测模型及敏感因子研究

为了探求泥石流平均流速敏感因子及影响因素耦合关系,本文采用BP神经网络和支持向量机模型对蒋家沟泥石流数据进行预测,对两种泥石流平均流速预测模型的学习与泛化能力进行比较,并对平均流速各影响因素的敏感程度进行分析,建立了泥石流平均流速敏感因子预测模型。结果表明:支持向量机的泛化能力优于BP网络,更适合样本数量较少的泥石流动态预测。沟道比降和不稳定层厚度是泥石流平均流速的主要影响因子,各因子之间存在复杂的耦合关系。基于不稳定层厚度和泥面比降的泥石流平均流速预测模型精度较高,能够定量描述泥石流动态与影响因子间的响应关系。研究成果可为泥石流防治提供依据。     

基于混合核函数PSO-LSSVM的边坡变形预测

支持向量机（SVM）的核函数类型和超参数对边坡位移时序预测的精度有重要影响。鉴于局部核函数学习能力强、泛化性能弱,而全局核函数泛化性能强、学习能力弱的矛盾,通过综合两类核函数各自优点构造了基于全局多项式核和高斯核的混合核函数,并引入粒子群算法（PSO）对最小二乘支持向量机（LSSVM）超参数进行全局寻优,提出了边坡位移时序预测的混合核函数PSO-LSSVM模型。将模型应用于锦屏一级水电站左岸岩石高边坡变形预测分析,并与传统核函数支持向量机预测结果进行对比分析。结果表明,该模型较传统方法在预测精度上有了明显提高,预测结果科学可靠,在边坡位移时序预测中具有良好的实际应用价值。     

滑坡位移时序预测的核函数构造

获得支持向量机(SVM)背景下滑坡位移时序准确预测的关键,是构造或选择一合适的核函数。通过分析滑坡位移时序曲线特征以及不同类型Mercer核的性质,从基于核函数上的封闭运算角度,构造出支持向量机背景下预测滑坡位移时序的最佳核函数。利用3组不同特征的滑坡位移时序,对构造出的核函数进行性能检验,数值实验表明：对于典型的3组滑坡时序,LPG与MPG核的学习性能要优于简单核,且前者适合复杂位移时序的回归预测,而后者更适合规律性较强的简单时序曲线的建模预测。此外,探讨了这两种核函数下的核参数取值对模型精度的影响。     

基于支持向量机-马尔可夫链的位移时序预测

结合支持向量机和马尔可夫链,提出了一种新的位移时序预测模型--支持向量机－马尔可夫链预测模型（SVM-MC）。通过对实测位移值的学习,利用经粒子群算法优化的支持向量机对位移时间序列的宏观发展趋势进行滚动预测;在此基础上应用马尔可夫链确定位移时序的状态转移概率矩阵,通过对状态的划分、实测值与支持向量机拟合值的绝对误差及相对误差等指标的分析,实现了对预测结果的改进。将该模型应用到某工程永久船闸高边坡的位移时序预测中,结果表明,该模型具有科学可靠、预测精度高的优点,在岩土体位移时序预测中具有有一定工程应用价值。     

地下结构孕险环境危险性预警的改进SVM模型

针对地震作用下孕险环境样本数据有限、复杂非线性等特点,提出了一种基于最小二乘支持向量机的地下结构孕险环境危险性预警方法。围绕地震后效应的地下工程区域孕险环境危险性预警目标和特征参数,设计孕险环境危险性问题的向量机表示形式,提出孕险环境危险性支持向量机训练工作机制,利用支持向量机结构风险最小化原则和非线性映射特性,建立基于最小二乘支持向量机的地下结构孕险环境危险性预警模型及其算法组件,并利用遗传算法优化其惩罚函数和核函数参数,隐式表达孕险环境危险性与其影响因素之间的非线性关系。结果表明,模型具有有效的小样本学习能力,具有较高的拟合和预测精度,明显优于神经网络等预测模型。     

基于遗传支持向量机的地下工程裂隙岩体注浆量预测

提出了地下工程裂隙岩体注浆量预测的遗传支持向量机方法,通过支持向量机对实际注浆数据样本进行学习,建立注浆量及其影响因素之间的非线性映射关系,基于这种关系实现注浆量的预测。模型建立过程中,考虑到支持向量机惩罚因子和核参数对预测精度的影响,以预测误差为适应度,采用遗传算法对最佳参数进行搜索。结果表明,本文方法计算快速,预测精度高,是一种注浆量预测的好方法。     

基于邻域粗糙集和支持向量机的固结系数预测

采用邻域粗糙集和支持向量机建立滹沱河某地区软土固结系数的预测模型。基于自行改装的渗透固结仪,利用公式法确定不同压力下的固结系数。通过室内试验确定土体的指标参数,采用邻域粗糙集对该指标参数进行属性约简,将约简后的指标参数作为影响因素,分别建立支持向量机和神经网络的固结系数预测模型,预测未知样本的固结系数,并与实测值进行对比。结果表明:公式法可以准确客观地确定固结系数;支持向量机和BP神经网络建立的该地区软土固结系数预测模型均可以预测区域内未知点的固结系数,且支持向量机方法的预测精度比神经网络方法的预测精度提高了约10%。本文提出的方法直接从实验数据出发,通过易获取的影响因素建立特定地区固结系数预测模型,并可预测该区域其余未知点的固结系数。     

支持向量机在砂土液化预测中的应用研究

介绍了人工智能领域最新的基于结构风险最小化原理的数据挖掘算法——支持向量机算法。根据支持向量机线性分类和可以具有不同核函数的非线性分类两种算法，建立了砂土液化预测模型，并且运用Matlab语言编写了程序。通过试算和分析比较得到了最佳模型，最佳模型的预测结果与实际液化情况基本上一致。认为支持向量机算法无论在学习或者预测精度方面都有很大的优越性，而基于支持向量机理论建立的砂土液化预测模型是可行的，且可以较为准确地实现砂土液化的预测。     

基于支持向量机的水库诱发地震分析

将诱发水库地震的主要因素（岩性、岩体完整性、断层性质、库区区域应力状态、库区地震活动背景）划分为11个因子,并进行定量化;再根据每个样本到所属类内超平面的距离计算每个样本点的模糊因子,确定其对分类超平面影响大小;然后建立水库地震的支持向量机（SVM）和模糊支持向量机（FSVM）模型,并应用于水库诱发地震等级预测。实例分析表明,两种模型均可用于水库诱发地震等级预测,具有预测精度较高、考虑因素全面的特点,相比之下SVM模型预测结果略优于FSVM模型。另外,在应用SVM和FSVM进行分类时,如果样本离散性较高,则SVM模型优于FSVM模型;相反,如果样本离散性较低,则FSVM模型优于SVM模型。     

Identification of algal blooms based on support vector machine classification in Haizhou Bay, East China Sea

Harmful algal blooms commonly known as red tides have been observed at increasing frequencies, which are causing serious economic and ecologic problems in Haizhou Bay off the eastern coast of China. It is important to study the inducing factors of red tides including a wide variety of environmental variables and the complex interactions between them. This study explores the possibility of predicting the occurrence of red tides using support vector machine (SVM) with environmental variables. Seventeen in situ environmental variables which are known to affect the occurrence of red tides were collected between May and October of 2004–2006. Seven characteristic factors were extracted from these variables via factorial analysis to reduce computation complexity. Three of them are related to nutrients, others are contributed by temperature, oxygen depletion, pH, hydrodynamics, and precipitation, respectively. The classification models based on SVM were constructed to identify the red tides samples using the seven factors as independent variables and radial basis function as the kernel function. The model with the combination parameters of C = 10, γ = 0.7, and ζ = 0.1 has the highest accuracy of 92.06 %. It indicates that the model is highly valuable in predicting the occurrence of red tides by environmental variables in this region for its conservative threshold of surface algae concentration.     

Specification and prediction of nickel mobilization using artificial intelligence methods

Groundwater and soil pollution from pyrite oxidation, acid mine drainage generation, and release and transport of toxic metals are common environmental problems associated with the mining industry. Nickel is one toxic metal considered to be a key pollutant in some mining setting; to date, its formation mechanism has not yet been fully evaluated. The goals of this study are 1) to describe the process of nickel mobilization in waste dumps by introducing a novel conceptual model, and 2) to predict nickel concentration using two algorithms, namely the support vector machine (SVM) and the general regression neural network (GRNN). The results obtained from this study have shown that considerable amount of nickel concentration can be arrived into the water flow system during the oxidation of pyrite and subsequent Acid Drainage (AMD) generation. It was concluded that pyrite, water, and oxygen are the most important factors for nickel pollution generation while pH condition, SO₄, HCO₃, TDS, EC, Mg, Fe, Zn, and Cu are measured quantities playing significant role in nickel mobilization. SVM and GRNN have predicted nickel concentration with a high degree of accuracy. Hence, SVM and GRNN can be considered as appropriate tools for environmental risk assessment.     

Evaluating the support vector machine for suspended sediment load forecasting based on gamma test

Due to the various influencing factors on river suspended sediment transportation, determining an appropriate input combination for developing the suspended sediment load forecasting model is very important for water resources management. The influence of pre-processing of input variables by Gamma Test (GT) was investigated on performance of Support Vector Machine (SVM) with two kernels; Radial Basis Function (RBF) and polynomial in order to forecast daily suspended sediment amount in the period between 1983 and 2014 at Korkorsar basin, northern Iran. The best input combination was identified using GT and correlation coefficient analysis. Then, the SVM model was developed and the suspended sediment amount was forecasted with RBF and polynomial kernels. The obtained results in testing phase showed that GT-SVM (RBF kernel) model can estimate suspended sediment more accurately with the lowest RMSE (14.045 ton/day), highest correlation coefficient (0.88) and highest NSEC coefficient (0.88) than SVM (RBF kernel) model (RMSE?=?18.36ton/day, \( {R}^2=0.79, \) \( NSEC=0.73 \)) and had a better performance than the other models. The results indicated that in forecasting the first nine maximum values of suspended sediment load, GT-SVM (RBF) had a higher capability than the other models and could provide a more accurate estimation from the maximum rate of suspended sediment. The results of this study showed the capability of identifying the priority of the input parameters can change GT to a useful and technical test for input variables pre-processing to forecast the amount of suspended sediments.     

Analysis of soil erosion characteristics in small watersheds with particle swarm optimization, support vector machine, and artificial neuronal networks

Sand production by soil erosion in small watershed is a complex physical process. There are few physical models suitable to describe the characteristics of the intense erosion in domestic loess plateau. Introducing support vector machine (SVM) oriented to small sample data and possessing good extension property can be an effective approach to predict soil erosion because SVM has been applied in hydrological prediction to some extent. But there are no effective methods to select the rational parameters for SVM, which seriously limited the practical application of SVM. This paper explored the application of intelligence-based particle swarm optimization (PSO) algorithm in automatic selection of parameters for SVM, and proposed a prediction model by linking PSO and SVM for small sample data analysis. This method utilized the high efficiency optimization property and swarm paralleling property of PSO algorithm and the relatively strong learning and extending capacity of SVM. For an example of Huangfuchuan small watershed, its intensive fragmentation and intense erosion earn itself the name of “worst erosion in the world”. Using four characteristics selection algorithms of correlation feature selection, the primary affecting factors for soil erosion in this small watershed were determined to be the channel density, ravine area, sand rock proportion, and the total vegetation coverage. Based on the proposed PSO–SVM algorithm, the soil erosion modulus in the small watershed was predicted. The accuracy of the simulation and prediction was good, and the average error was 3.85%. The SVM predicting model was based on the monitoring data of sand production. The construction of the SVM erosion modulus prediction model for the small watershed comprehensively reflected the complex mechanism of soil erosion and sand production. It had certain advantage and relatively high practical value in small sample prediction in the discipline of soil erosion.     

SVM在地下工程可靠性分析中的应用

将支持向量机应用到地下工程可靠性分析中,通过将支持向量机分别与一阶二次矩和蒙特卡洛结合,提出了基于支持向量机的可靠性分析方法,利用数值模拟构造学习样本,通过支持向量机学习,建立变形与随机变量之间映射关系的支持向量机表达,进而实现隧道极限状态函数及其偏导数的显式表达,从而计算隧道的可靠性指标。该方法避免了传统可靠性分析的缺点。算例分析结果表明,该方法计算效率高、结果可靠,对含有大量随机变量的复杂岩土工程可靠性分析具有很大的潜力,具有广泛的应用前景和工程价值。     

Application of uncertainty analysis to groundwater pollution modeling

Prediction and evaluation of pollution of the subsurface environment and planning remedial actions at existing sites may be useful for siting and designing new land-based waste treatment or disposal facilities. Most models used to make such predictions assume that the system behaves deterministically. A variety of factors, however, introduce uncertainty into the model predictions. The factors include model and pollution transport parameters and geometric uncertainty. The Monte Carlo technique is applied to evaluate the uncertainty, as illustrated by applying three analytical groundwater pollution transport models. The uncertainty analysis provides estimates of statistical reliability in model outputs of pollution concentration and arrival time. Examples are provided that demonstrate: (a) confidence limits around predicted values of concentration and arrival time can be obtained, (b) the selection of probability distributions for input parameters affects the output variables, and (c) the probability distribution of the output variables can be different from that of the input variables, even when all input parameters have the same probability distribution