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41.
《地学前缘(英文版)》2020,11(3):871-883
Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions. 相似文献
42.
支持向量机方法是基于统计学习理论提出的一种机器学习方法,在处理小样本、非线性问题方面有着很强的优势。而地震的孕育发生过程是一个复杂的非线性动力学系统,地震数据时间序列的变化更呈现非线性、不规则等特征。本文系统地分析了天津及其周边地区多种前兆现象,选取反映2至3个月短期情况的前兆测项,使用支持向量机分类方法建立地震综合预测模型,初步结果表明这种方法在地震短期预测中有一定的应用前景。 相似文献
43.
Xavier Emery 《Mathematical Geosciences》2008,40(6):671-688
An important aspect in mineral resource evaluation is the reduction of variance when post-processing the grade distributions
defined on the support (volume) of the available data into distributions defined on the support of the proposed selective
mining units. Although the volume-variance relationship is well understood for the estimation of global grade distributions,
it is still an unsolved issue for local estimation studies based on non-parametric geostatistical methods, such as indicator
kriging, for which the support correction is not inherent to the method. To clarify this relationship, the local change of
support problem is examined in the scope of two parametric models (multi-Gaussian and discrete Gaussian models). It is shown
that the variance reduction factor between point and block-support local distributions depends on the block being considered
and is less than the global variance reduction factor. As a consequence, post-processing the local point-support grade distributions
on the basis of the latter systematically understates the importance of the change of support at the local scale and makes
selective mining appear more economically attractive than it really is. In the light of these results, a methodology is proposed
to post-process the local point-support distributions obtained via non-parametric (indicator) methods into block-support distributions.
An application to simulated data indicates that this methodology provides an accurate estimation at the block support when
dealing with diffusion-type random fields. 相似文献
44.
支持向量机(SVM)的惩罚参数及核参数的选择直接影响到模型效果,通过粒子群算法(PSO)解决支持向量机的参数选择问题,实现了参数选择的自动化。将该方法应用于热带气旋强度预报,利用气候持续性因子,挑选了1990年的100个左右样本进行预报检验,预报时效为12 h、24 h、36 h、48 h的强度平均绝对误差分别为3.00、4.35、4.93和6.68 m/s。另外,还与国外预报结果及采用最小二乘回归法的预报结果进行了效果的比较,SVM方法显示了更好的预报能力。 相似文献
45.
多特征差分核支持向量机模型的湿地变化检测 总被引:1,自引:0,他引:1
文章通过对不同特征空间、不同时相信息和不同类别引入多核函数组合和类别加权方式,构建了基于多特征差分核的加权支持向量机变化检测模型,能够获得平衡样本大小、综合多特征空间的变化检测结果.以上海崇明岛东端地区为例,利用4期ETM+/TM影像数据,基于构建的SVM变化检测模型,最终得到研究区湿地资源的空间变化格局,结果表明检测模型能够有效、准确地提取多种湿地变化类别的空间分布信息;并从景观水平上对检测结果进行格局分析,为湿地保护和管理提供科学参考. 相似文献
46.
Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The
NGDC flares catalogue and the SOHO/LASCO CME catalogue are processed to associate X and M-class flares with CMEs based on
timing information. Automated systems are created to process and associate years of flare and CME data, which are later arranged
in numerical-training vectors and fed to machine-learning algorithms to extract the embedded knowledge and provide learning
rules that can be used for the automated prediction of CMEs. Properties representing the intensity, flare duration, and duration
of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to
a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks
(CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the
input of a flare’s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques
are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive
performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided. 相似文献
47.
The shortage of potassium salt seriously restricts the development of China's agriculture. Increasing the exploration and development of potash will help improve the self-sufficiency of potassium in China. With rich potassium salt resources, Sichuan basin is one of the most important research areas for potash exploration and development in China. Polyhalite is an important solid potassium salt mineral in Sichuan basin, often intercalated in rock minerals such as anhydrite, rock salt and dolomite. Aiming at the problem that conventional log interpretation methods are difficult to accurately identify polyhalites, this paper proposed a new Support Vector Machine (SVM) recognition method based on Particle Swarm Optimization (PSO) to classify polyhalites in Sichuan basin. Based on particle swarm optimization and support vector machine theory, combined with logging interpretation theory, the effective data sensitive to polyhalite logging response were selected as input samples to generate training sets and test sets randomly. The Radial Basis Function (RBF) parameters were optimized by particle swarm optimization, and the classification and prediction model of polyhalite was established. Compared with mud logging results, the recognition accuracy of SVM model based on particle swarm optimization reached 97.5758%, which is obviously better than that of SVM model optimized by cross validation method in recognition accuracy and speed. The results show that the model has broad application prospects in potash exploration in Sichuan basin. 相似文献
48.
判断矿床(点)的类型是矿床勘探中的重要内容,传统预测金矿成矿规模的方法不仅耗时耗力,而且所需的经济成本较大。为提高矿床规模的勘探效率和准确度,揭示元素与金矿成矿规模的潜在联系,文中提出了耦合主成分分析(principal component analysis,PCA)和支持向量机(support vector machine,SVM)算法的预测分析PCA-SVM(principal component analysis-support vector machine)方法。该方法先通过主成分分析提取数据中的主要特征,再将主要特征带入支持向量机算法,从而训练出最优分类器以预测金矿成矿规模。文中共使用了3 812个金矿样本数据用于学习训练和预测分析,训练准确率为92.3%,测试准确率为88.7%,分别比直接使用支持向量机算法高出14.3%和17.1%。基于PCA-SVM的预测模型,不仅消除了人为主观因素的影响,而且有效提高了勘探过程中矿床预测的准确率和矿床勘探的效率,为地质勘查工作提供依据。 相似文献
49.
《China Geology》2020,3(2):339-344
Considering the geological hazards attributed to the highway slope, using a common simple model cannot accurately assess the stability of the slope. First, principal component analysis (PCA) was conducted to extract the principal components of six factors (namely, bulk density, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio) affecting the slope stability. Second, four principal components were adopted as input variables of the support vector machine (SVM) model optimized by genetic algorithm (GA). The output variable was slope stability. Lastly, the assessing model of highway slope stability based on PCA-GA-SVM is established. The maximal absolute error of the model is 0.0921 and the maximal relative error is 9.21% by comparing the assessment value and the practical value of the test sample. The above studies are conducive to enrich the assessing model of highway slope stability and provide some reference for highway slope engineering treatment. 相似文献
50.
做好防雷减灾工作之我见 总被引:5,自引:4,他引:1
从六个方面的防雷减灾工作入手,重点介绍提高防雷工作重要性的认识、做好防雷的宣传工作、注意工作方式方法、争取相关部门的支持、树立良好形象、加强自身建设的的工作的思路与做法。 相似文献