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1.
Li Yunkai Tian Yingjie Ouyang Zhiyun Wang Lingyan Xu Tingwu Yang Peiling Zhao Huanxun 《Environmental Earth Sciences》2010,60(7):1559-1568
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. 相似文献
2.
基于AGA的SVM需水预测模型研究 总被引：1，自引：0，他引：1
需水预测是一个由城市人口、工业水平、社会经济水平共同作用的多因素、多层次的复杂非线性系统.其结果将直接影响受区域水资源承载力约束的产业结构、布局形态等决策.作为一种集中参数预报方法,支持向量机方法具有对未来样本的较好的泛化性能,对于这类资料缺乏、系统结构尚欠清晰的问题可以取得较好的模拟和预测结果.基于此,本文将支持向量机方法引入需水预测领域,建立了需水预测支持向量机模型.同时,本文将加速遗传算法和支持向量机方法耦合起来,构造了支持向量机模型参数的自适应优化算法.模型在珠海市的应用实例表明:与简单遗传算法比较,AGA的模型参数寻优效率更高;与BP神经网络模型相比,SVM模型较好地解决了小样本、经验性等问题,并取得了较高的预测精度. 相似文献
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4.
M. J. Alizadeh A. Shabani M. R. Kavianpour 《International Journal of Environmental Science and Technology》2017,14(11):2399-2410
A reliable prediction of dispersion coefficient can provide valuable information for environmental scientists and river engineers as well. The main objective of this study is to apply intelligence techniques for predicting longitudinal dispersion coefficient in rivers. In this regard, artificial neural network (ANN) models were developed. Four different metaheuristic algorithms including genetic algorithm (GA), imperialist competitive algorithm (ICA), bee algorithm (BA) and cuckoo search (CS) algorithm were employed to train the ANN models. The results obtained through the optimization algorithms were compared with the Levenberg–Marquardt (LM) algorithm (conventional algorithm for training ANN). Overall, a relatively high correlation between measured and predicted values of dispersion coefficient was observed when the ANN models trained with the optimization algorithms. This study demonstrates that the metaheuristic algorithms can be successfully applied to make an improvement on the performance of the conventional ANN models. Also, the CS, ICA and BA algorithms remarkably outperform the GA and LM algorithms to train the ANN model. The results show superiority of the performance of the proposed model over the previous equations in terms of DR, R 2 and RMSE. 相似文献
5.
风化花岗岩声谱特征分析 总被引：7，自引：0，他引：7
岩石材料可视为声波的天然低通滤波器 ,声波在不同力学性质岩石材料中传播时 ,由于岩石对声波吸收程度不等因而高频声波滤除状况也不同。接收波中频率丰富程度与岩 石 的力学性质密切相关,岩石力学强度愈高,声谱中高频成分就愈丰富。反之,声谱中主要为 低频,高频分量因衰减而缺失。实验采用全数字化DB4型多波参数分析仪和计算机,对波形 数据进行FFT分析,通过接收谱和频率响应的特征来研究岩石的力学性质。声衰减研究可为 岩石、岩体强度和综合评价提供更多、更可靠的信息,是解决工程地质风化岩问题研究的一 种可行方法。 相似文献
6.
Enhanced demand for coal and minerals in the country has forced mine operators for mass production through large opencast mines. Heavy blasting and a large amount of explosive use have led to increased environmental problems, which may have potential harm and causes a disturbance. Ground vibrations generated due to blasting operations in mines and quarries are a very important environmental aspect. It is clear that a small amount of total explosive energy is being utilized in blasting for breakage of rock mass, while the rest is being wasted. The amount of energy which is wasted causes various environmental issues such as ground vibrations, air overpressure, and fly rock. Ground vibrations caused by blasting cannot be eliminated entirely, yet they can be minimized as far as possible through a suitable blasting methodology. A considerable amount of work has been done to identify ground vibrations and assess the blast performance regarding the intensity of ground vibrations, i.e., peak particle velocity and frequency spectrum. However, not much research has done into reducing the seismic energy wasted during blasting leading to ground vibrations. In this paper, the blast-induced ground vibrations in three orthogonal directions, i.e., transverse, vertical, and longitudinal, were recorded at different distances using seismographs. An attempt has been made for the estimation of the percentage of explosive energy dissipated in the form of seismic energy with electronic and non-electric (NONEL) initiation system. signal processing techniques with the help of DADiSP software is used to study the same. 相似文献
7.
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。 相似文献
8.
横波速度对于地震模拟、AVO分析以及流体识别具有重要意义,实际测井数据中横波速度信息缺乏,因此横波速度预测已经成为岩石物理研究的一个焦点。综合Xu-White模型以及Pride模型,提出了一种新的用于计算干岩石模量的岩石物理模型。该模型综合考虑了孔隙形状和成岩作用对干岩石体积模量、剪切模量的影响,因此该模型更加合理并具有更高的精度。同时联合Gassmann理论,建立了饱和流体岩石的纵波、横波速度计算模型。将该模型成功地应用于实验室测量数据和实际测井数据的横波速度预测中,预测结果表明,基于本文提出的岩石物理模型的横波速度预测方法是行之有效的。 相似文献
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10.
Prediction of Safe Charge to Protect Surrounding Structures Using Support Vector Machine 总被引：1，自引：0，他引：1
Manoj Khandelwal 《Geotechnical and Geological Engineering》2012,30(4):859-867
The present paper mainly with deals the prediction of safe explosive charge used per delay (QMAX) using support vector machine (SVM) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). 150 blast vibration data sets were monitored at different vulnerable and strategic locations in and around a major coal producing opencast coal mines in India. 120 blast vibrations records were used for the training of the SVM model vis-à-vis to determine site constants of various conventional vibration predictors. Rest 30 new randomly selected data sets were used to compare the SVM prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R) between measured and predicted values of safe charge of explosive used per delay (QMAX). It was found that coefficient of correlation between measured and predicted QMAX by SVM was 0.997, whereas it was ranging from 0.063 to 0.872 by different conventional predictor equations. 相似文献
11.
矿井涌水量的准确预测对预防矿山透水事故的发生至关重要,提出利用GA优化的SVM模型（GA-SVM）来实现矿井涌水量的短期准确预测。该方法利用GA的自动寻优功能寻找SVM的最佳参数,提高了预测的准确率。首先,利用微熵率法求矿井涌水量时间序列的最佳嵌入维数和延迟时间,进行相空间重构。其次,采集义煤集团千秋煤矿2011—2015年实际涌水量的时间序列,利用GA-SVM模型对最后12组数据进行预测,其预测平均绝对百分比误差仅为0.92%,最大相对误差为2.62%。最后,与PSO-SVM和BP神经网络预测进行对比,结果表明GA-SVM优化模型适用于矿井涌水量的预测并且预测精度较高。 相似文献
12.
针对遗传算法（GA）存在早熟现象和局部寻优能力较差等缺陷,引入具有很强局部搜索能力的模拟退火算法（SA）,组成改进的遗传模拟退火算法（GSA）提高优化问题的能力和求解质量。针对BP神经网络容易陷入局部最小和收敛速度慢等方面的不足,应用改进的遗传模拟退火算法搜索BP神经网络的最优权值和阀值,提高BP神经网络的预测精度,建立了围岩力学参数反分析的GSA-BP神经网络模型。将该模型应用于乌东德水电站右岸地下厂房围岩力学参数的反演分析中,根据监测围岩变形数据反演围岩力学参数,反演所得参数应用到正计算分析中,得出的计算位移与实测值吻合较好,说明该方法的有效性和应用于该工程的可行性。 相似文献
13.
基于遗传-神经网络的单桩竖向极限承载力预测方法研究 总被引：15，自引：4，他引：11
分析了BP神经网络的缺陷和遗传算法的特点,建立了基于遗传-BP神经网络的单桩竖向极限承载力预测模型。实例研究表明,预测模型性能良好、预测精度提高、简便易行、行之有效,该方法具有广泛的应用前景。 相似文献
14.
基于光谱相似尺度的支持向量机蚀变信息提取 总被引：4，自引：0，他引：4
文章提出一种基于光谱相似尺度(spectral similarity scale-SSS)的支持向量机(support vector machines-SVM)遥感数据矿化蚀变信息提取的新方法.该方法选择青海两兰地区作为遥感矿化蚀变信息典型研究区,利用该区域的Landsat7ETM遥感影像结合地面实况调查数据,从图像上选取少量具有代表性的样本点的光谱作为参考光谱,利用SSS方法提取训练样本,然后应用SVM算法进行遥感矿化蚀变信息提取.试验结果经野外检查和验证,效果良好. 相似文献
15.
T. G. Sitharam Pijush Samui P. Anbazhagan 《Geotechnical and Geological Engineering》2008,26(5):503-517
Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock
is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site
amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore,
India is arrived from the 652 boreholes data in the area covering 220 km2. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability
of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector
Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of
rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore,
where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the
Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation
algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine
based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the
reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced
level of rock has been presented and discussed. 相似文献
16.
In the predicting of geological variables, artificial neural networks (ANNs) have some drawbacks including possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters and the components of its complex structure. Recently, support vector machines (SVM) has been found to be popular in prediction studies due to its some advantages over ANNs. Because the least squares SVM (LS‐SVM) provides a computational advantage over SVM by converting quadratic optimization problem into a system of linear equations, LS‐SVM method is also tried in study. The main purpose of this study is to examine the capability of these two SVM algorithms for the prediction of tensile strength of rock materials and to compare its performance with ANN and linear regression (MLR) models. Total porosity, sonic velocity, slake durability index and aggregate impact value were used as input in modeling applications. Favorite performance evaluation measures were employed to assess developed models. The results determined in study indicate that the SVM, LS‐SVM and ANN methods are successful tools for prediction of tensile strength variable and can give good prediction performances than MLR model. Although these three methods are powerful artificial intelligence techniques, LS‐SVM makes the running time considerably faster with the higher accuracy. In terms of accuracy, the LS‐SVM model resulted in error reductions relative to that of the other models. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
17.
为探究注采参数对松辽盆地干热岩物理力学及波动特征的影响,对不同注采参数下高温遇水冷却后花岗岩进行纵、横波波速测试试验和抗压强度试验。分别考虑注采参数（岩样温度、水温、高温遇水循环次数） 与岩样物理力学特征（外观形态、峰值强度、弹性模量、泊松比）、波动特征（纵、横波波速） 的关联性,建立不同注采参数下力学特征与波动特征拟合曲线,并研究搁置过程中不同岩样温度、不同水温条件下岩体物理力学及波动特征变化规律。研究发现：（1） 搁置初期,岩样温度越高,质量、纵、横波波速、弹性模量降幅越大;水温升高,质量、纵、横波波速、弹性模量降幅先增大后减小。（2） 对采热过程中岩体物理力学及波动特征影响由大到小的注采参数依次为靶区温度、注水循环次数、注水温度。提升岩样温度、增加注水循环次数,岩样力学与波动特征均逐渐下降,提高注水温度变化规律与其相反;经历600℃高温,岩样纵波波速、横波波速、峰值强度、弹性模量降幅分别达到53.44%、58.02%、66.56%、79.84%,高温遇水循环5 次
后降幅依次达到33.61%、33.63%、34.22%、56%。（3） 影响岩样力学与波动特征关联性的注采参数由大到小依次为岩样温度、高温遇水循环次数、水温。此研究能够为松辽盆地热采注采参数的选取提供一定参考。 相似文献
18.
针对TSP预报工作耗时长、经验依赖性强等问题,以提高TSP探测精度与效率为目标,分析了影响TSP数据采集与处理精度的关键因素,总结了提高TSP探测效率的经验。结合千岛湖配水工程某过江隧洞段地质超前预报工程,在TSP探测分辨率下降的区域开展TSP跟踪预报或地质雷达探测工作,对物探异常区辅以钻探验证。结果表明:岩体完整性差地段的TSP纵波波速、密度及各力学模量值偏低,TSP对破碎带探测较敏感,对基岩裂隙水识别能力相对较弱,纵、横波波速均偏低的区域岩体富水的概率更大;岩体破碎含水区段雷达电磁异常特征表现为反射波振幅强,同相轴错断,主频偏低。综合预报成果发挥了各种预报方法优势互补的作用,为隧洞支护及超前注浆方案的优化提供了重要参考。 相似文献
19.
Drilling and blasting is a major technology in mining since it is necessary for the initial breakage of rock masses in mining. Only a fraction of the explosive energy is efficiently consumed in the actual breakage and displacement of the rock mass, and the rest of the energy is spent in undesirable effects, such as ground vibrations. The prediction of induced ground vibrations across a fractured rock mass is of great concern to rock engineers in assessing the stability of rock slopes in open pit mines. The waveform superposition method was used in the Gol-E-Gohar iron mine to simulate the production blast seismograms based upon the single-hole shot vibration measurements carried out at a distance of 39 m from the blast. The simulated production blast seismograms were then used as input to predict particle velocity time histories of blast vibrations in the mine wall using the universal distinct element code (UDEC). Simulated time histories of particle velocity showed a good agreement with the measured production blast time histories. Displacements and peak particle velocities were determined at various points of the engineered slope. The maximum displacement at the crest of the nearest bench in the X and Y directions was 26 mm, which is acceptable in regard to open pit slope stability. 相似文献
20.
针对新型核电工程结构AP1000核岛结构设计地基中的5类非坚硬岩场地,即硬岩场地、软岩场地、上限软-中等土场地、软-中等土场地和软土场地,采用一维土层场地模型开展场地土和计算基底条件对设计地震动影响计算分析。分析中,场地模型的计算基底剪切波速分别取为700、1 100、2 438 m/s,计算基底输入地震动分别选择基于核电建设相关技术文件和规范规定的反应谱RG1.60谱、AP1000谱和HAD101/01谱（5个阻尼比）合成的人工地震动时程。计算分析表明：非坚硬岩场地会导致场地地震动峰值加速度及频谱特性显著变化,场地越软影响程度越显著;除软土场地外,场地对地震动峰值加速度和反应谱的影响均为放大作用,软土场地对地震动较低频段反应谱有放大作用,但对峰值加速度和较高频段反应谱具有强烈的减小作用;对于各类场地,计算基底及其剪切波速的变化均会导致地表地震动峰值及频谱特性明显甚至显著变化,其影响程度与计算基底剪切波速成正比;随着场地由硬变软,计算基底剪切波速的变化对场地地震动的影响程度大为减小,至软土场地几乎不产生影响。考虑到场地类型及计算基底选取对场地地震动的显著影响,我国核电厂建设引用AP1000标准设计时应合理分析场地的适宜性。 相似文献