How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?     

《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   

基于支持向量机的京津冀城市群热环境时空形态模拟   总被引：1，自引：0，他引：1  

杨浩  孟娜  王婧  郑燕  赵莉 《地球信息科学学报》2019,21(2):190-200

城市群热环境作为区域生态重要组成部分,已成为近年来的研究热点。而如何选择针对城市群这种复杂地地貌特征的热环境量化工具一直是亟待解决的技术难点,基于此本研究提出了一种解决多样本、非线性、非平稳及高维函数拟合的计算方法,并建立了基于支持向量机(SVM)的京津冀城市群热环境曲面模型来揭示城市群热环境的时空形态变化。研究结果表明：① SVM模型在刻画多核心、多种土地利用类型城市群热环境的空间分布方面具有理论与实践可行性,能够根据热环境的整体空间布局通过高斯核函数进行局部优化差值,最大限度减少缺省值对模型拟合结果的影响。相比于对照方法可以模拟出更高精度的复杂地貌特征城市群热岛空间分布格局;② 在SVM模型曲面拟合的过程中,拟合精度和拟合时间是衡量拟合结果的重要指标,而原始影像的分辨率则是影响该指标的决定性因素;③ 2003-2013年区域内北京市与天津市的城市热岛效应变化最为明显,热岛面积分别增加7091 km²与4196 km²,空间上呈现出逐年接近连片发展趋势,热岛重心移动轨迹具有明显的时空分异性。北京城市热岛特征为东南部地区异速增长,西部地区缓慢增长;天津城市热岛特征为以城市中心为圆心向周围扩展。本研究进一步丰富了城市群热环境评测的定量方法,可以在实践上对城市群的城市规划、城市建设、环境保护和区域可持续发展等提供定量化、可视化的决策支持。  相似文献   

基于高分遥感影像的建筑物侧面信息提取及其高度计算     

宋旭东  曹文峰  冯德俊  张星星 《北京测绘》2020,(3):296-300

本文选取成都市某一区域建筑物A、B为研究对象,采用分辨率为0.61 m的Quick bird影像,运用图像分割法和LVQ神经网络算法,提取建筑物侧面信息,根据假设法原理,构建高度计算物理模型,求取建筑物高度。对比实测数据,结合可能影响实验结果的实地因素、遥感影像因素进行精度分析与评价,探讨基于高分遥感影像的建筑物侧面信息提取和高度计算的方法。结果表明,LVQ神经网络算法在建筑物侧面提取和高度计算中有更好的应用价值,精度高达94%。  相似文献   

Assessing model of highway slope stability based on optimized SVM     

《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   

Backward automatic calibration for three-dimensional landslide models     

Giacomo Titti  Giulia Bossi  Gordon G.D.Zhou  Gianluca Marcato  Alessandro Pasuto 《地学前缘(英文版)》2021,12(1):231-241

Back-analysis is broadly used for approaching geotechnical problems when monitoring data are available and information about the soils properties is of poor quality.For landslide stability assessment back-analysis calibration is usually carried out by time consuming trial-and-error procedure.This paper presents a new automatic Decision Support System that supports the selection of the soil parameters for three-dimensional models of landslides based on monitoring data.The method considering a pool of possible solutions,generated through permutation of soil parameters,selects the best ten configurations that are more congruent with the measured displacements.This reduces the operator biases while on the other hand allows the operator to control each step of the computation.The final selection of the preferred solution among the ten best-fitting solutions is carried out by an operator.The operator control is necessary as he may include in the final decision process all the qualitative elements that cannot be included in a qualitative analysis but nevertheless characterize a landslide dynamic as a whole epistemological subject,for example on the base of geomorphological evidence.A landslide located in Northeast Italy has been selected as example for showing the system potentiality.The proposed method is straightforward,scalable and robust and could be useful for researchers and practitioners.  相似文献   

基于支持向量机的围岩定性智能分级研究     

牛文林  李天斌  熊国斌  张广洋 《工程地质学报》2011,19(1):88-92

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...  相似文献   

自动剪断线算法的设计与实现     

李春光  赵怡康 《山东地质》2011,(8):31-34

结合相关几何学原理,阐述了线段之间存在的空间拓扑关系,并结合当前线段相交算法的优缺点,提出了判断线段间有唯一实交点的混合判断方法,并在此基础上完成了对自动剪断线算法的设计与实现。  相似文献   

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   总被引：1，自引：0，他引：1  

顾锦荣  刘华强  刘向陪  吕庆平 《海洋预报》2011,28(3)

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%。  相似文献   

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation     

Dhaval Vyas  N.S.R. Krishnayya  K.R. Manjunath  S.S. Ray  Sushma Panigrahy 《International Journal of Applied Earth Observation and Geoinformation》2011

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.  相似文献   

基于模式识别理论的卡钻类型判别分析          下载免费PDF全文

吴军  臧艳彬  陈星星 《探矿工程》2015,42(10):31-34

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