基于SVM的地下采煤区沉陷灾害发育敏感性分区研究

地下采煤区沉陷灾害发育重点区预测目前尚无固定程式,且敏感区预测结果存在不确定性较大的问题。以山西省太原市西山地区沉陷灾害为研究对象,分别以2012年和2014年核查编录的沉陷灾害数据为建模数据和验证数据,以高程、坡度、坡向、地势起伏度、地面曲率、地层岩组、地质构造为敏感性评价因子,综合运用GIS空间分析、统计分析和支持向量机(SVM)等方法,构建了4种核函数SVM沉陷灾害敏感性分区预测模型,分别从模型的评价因子权重、模型优选、敏感性分区预测结果、预测精度和模型适用性进行了分析。结果表明:多项式核函数SVM模型(PL-SVM)的训练精度(受试者特征曲线下面积AUC=0.854)与验证精度(AUC=0.755)均较高,模型预测能力良好,是4种模型中表现最好的模型,所划分敏感性分区结果合理,极高与高敏感区以较小面积分布较多沉陷灾害点,而低敏感区则以较大面积分布极少沉陷灾害点。PL-SVM模型预测的太原西山地区沉陷灾害发育极高、高、中和低敏感区的面积占比分别为:20.19%、17.43%、21.18%、41.20%,频率比值与敏感性等级之间呈良好的正相关,符合线性函数关系。PL-SVM模型敏感性评价结果可靠,适用性好,对地下采煤区沉陷灾害发育特征研究及灾害普查重点区预判具有参考意义。      

基于SOM-I-SVM耦合模型的滑坡易发性评价

在使用机器学习模型对滑坡进行易发性评价时,通常会在滑坡影响范围之外随机选取非滑坡样本点,具有一定的误差。为了提高滑坡易发性评价的精度,将自组织映射（self-organizing map,SOM）神经网络、信息量模型（information,I）以及支持向量机模型（support vector machine,SVM）进行耦合,提出一种基于SOM-I-SVM模型的滑坡易发性评价方法,并将SOM神经网络与K均值聚类算法进行对比,验证模型的可靠性。以十堰市茅箭区为例,首先通过对环境因子的相关性及重要性分析,筛选出距水系距离、坡度、降雨量、距构造距离、相对高差、距道路距离、地层岩性等7个因子,建立滑坡易发性评价指标体系,在此基础上计算出各因子的分级信息量值,并作为模型的输入变量进行滑坡易发性评价。分别采用SOM神经网络和K均值聚类算法选取非滑坡样本,然后将样本数据集代入I-SVM模型预测滑坡易发性。将SVM、I-SVM、KMeans-I-SVM、SOM-I-SVM等4种模型预测精度进行对比,其ROC曲线下面积（AUC）分别为0.82,0.88,0.90,0.91,说明SOM-I-SVM模型能...     

滑坡易发性预测不确定性:环境因子不同属性区间划分和不同数据驱动模型的影响

对于滑坡易发性预测建模，连续型环境因子在频率比分析时的属性区间划分数量（attribute interval numbers，AIN）和不同易发性预测模型是两个重要不确定性因素.为研究这两个因素对建模的影响规律，以江西省上犹县为例，考虑5种连续型环境因子AIN划分（4、8、12、16及20）和5种数据驱动模型（层次分析法（analytic hierarchy process，AHP）、逻辑回归（logistic regression，LR）、BP神经网络（back-propagation neural network，BPNN）、支持向量机（support vector machine，SVM）和随机森林（random forest，RF）），总计25种不同工况下的滑坡易发性预测研究.再开展滑坡易发性指数的不确定性（包括精度评价和统计规律等）分析.结果表明:（1）对于同一模型，随着AIN值从4增加至8再到20时，易发性预测精度先逐渐提升，然后缓慢提升直至稳定；（2）对于同一AIN值，RF模型预测精度最高，其后依次为SVM、BPNN、LR和AHP模型；（3）在25种组合工况下，AIN=20和RF模型的预测精度最高，AIN=4和AHP模型精度最低，但在AIN=8和RF模型组合下的易发性建模效率较高且精度也较高；（4）更大的AIN值和更先进的机器学习模型预测出的滑坡易发性指数的不确定性相对较低，更符合实际的滑坡概率分布特征.在环境因子属性区间划分为8和RF模型工况下高效准确地构建滑坡易发性预测模型.      

基于3种不同机器学习算法的滑坡易发性评价对比研究

准确的滑坡易发性评价结果是山区滑坡灾害防治的关键,可有效规避潜在滑坡带来的风险。为获得准确、可靠的滑坡预防参考,笔者以云南芒市为研究对象,选取高程、地层岩性、年均降雨量等9项评价因子,通过多重共线性分析,构建研究区滑坡易发性评价指标体系。分别基于支持向量机(SVM)、BP神经网络和随机森林(RF)3种典型机器学习算法进行滑坡易发性评价。利用准确性(ACC)、ROC曲线下面积(AUC)、滑坡比(S_ei)及野外实地考察对模型评价结果精度进行对比验证分析。结果显示RF模型的ACC、AUC和极高易发区的S_eV值最高,分别为0.867、0.94、9.21;BP神经网络模型次之,其S_eV值分别为0.829、0.90、9.14;SVM最低,其S_eV值分别为0.794、0.88、6.85。此外,RF算法所得结果还与实地考察情况保持了较高的一致性。实验结果表明与其他两种算法相比,RF算法在芒市区域具有更高的准确性和可靠性,更适合用于该区域的滑坡易发性建模,且利用该模型获得的评价结果,能够为芒市区域的滑坡防治提供理论依...     

灰色GM（2,1）模型在滑坡变形预测中的应用

应用灰色模型对滑坡变形进行预测,目前常用灰色GM(1,1)模型,而灰色GM(2,1)模型应用较少.在实际建模中发现,取不同长度的数据序列,建立的模型也不一样,所得的预测结果也有所不同.针对上述问题,本文基于统计的方法,得出白店子滑坡灰色预测模型最佳数据序列长度,在此基础上建立GM(2,1)模型对该滑坡深部位移进行预测,并与GM(1,1)模型预测结果进行了对比.结果表明,总体精度上GM(2,1)模型略高,预测误差较小,有很好的应用价值.     

基于机器学习的滑坡易发性预测建模及其主控因子识别

不同机器学习预测滑坡易发性的建模过程及其不确定性有所差异,另外如何有效识别滑坡易发性的主控因子意义重大.针对上述问题,以支持向量机(support vector machine,简称SVM)和随机森林(random forest,简称RF)为例探讨了基于机器学习的滑坡易发性预测及其不确定性,创新地提出了"权重均值法"来...     

基于贝叶斯优化机器学习超参数的滑坡易发性评价

利用机器学习模型进行滑坡易发性评价时,不同的超参数设置往往会导致评价结果的不同.采用贝叶斯算法对4种常见机器学习模型(逻辑回归LR、支持向量机SVM、人工神经网络ANN和随机森林RF)的超参数进行了优化,探索了该算法对滑坡易发性机器学习模型的优化效果.以湘中地区4县(安化县、新华县、桃江县和桃源县)滑坡易发性评价为例说...     

基于贡献率权重法的区域滑坡影响因子敏感性分析

滑坡影响因子是区域滑坡危险性评价的基础,而因子的敏感性直接反应评价过程中权重大小,因此因子敏感性的精度将影响着评价结果的精度。本文以平昌县滑坡灾害为例,选取坡度、地层、高差、高程、坡形五个因子作为滑坡灾害的敏感性分析因子,采用贡献率权重法对研究区滑坡灾害影响因子的内部敏感性及因子间敏感性进行分析,该评价方法结构简单,不受地域限制,能客观的反应出各评价因子间以及因子内对地质灾害的敏感性。分析结果显示:选取的5个指标敏感性大小依次为:坡形坡度地层高差高程;在各评价指标内部中,坡度10°~30°、侏罗系下统蓬莱镇组上段地层、高差4.94~12.36 m、高程300~800 m、凸形与直线形坡是滑坡发生的高敏感区间。     

逻辑回归与支持向量机模型在滑坡敏感性评价中的应用

白龙江流域是我国滑坡泥石流灾害四大高发区之一,进行该区域滑坡敏感性评价,能够为决策者在灾害管理和设施建设规划方面提供帮助,对区域防灾减灾具有重要指导意义。本研究采用边坡单元为基本研究单元,在野外调查及前人研究基础上,选择控制该区域滑坡发育的19个要素作为影响因子;经过主成分分析和独立性检验得到该区域对滑坡形成贡献最大的6个因子:高程、坡度、坡向、岩性、断裂距离和人口密度;分别使用二元逻辑回归模型(LR)和支持向量机模型(SVM)对该区域进行滑坡敏感性评价;最后,采用ROC曲线对模型精度进行验证。研究结果表明,两模型各能将38.76%、14.48%、9.40%、11.28%、26.07%和13.49%、21.61%、8.17%、26.70%、30.04%的边坡单元分别预测为极高危险区、高危险区、中度危险区、低危险区和极低危险区;精度验证结果表明两种模型均能有效地进行该区域滑坡敏感性评价,并且支持向量机模型具有更好的分类能力、预测精度和稳定性。     

逻辑回归与支持向量机模型在滑坡敏感性评价中的应用

白龙江流域是我国滑坡泥石流灾害四大高发区之一,进行该区域滑坡敏感性评价,能够为决策者在灾害管理和设施建设规划方面提供帮助,对区域防灾减灾具有重要指导意义。本研究采用边坡单元为基本研究单元,在野外调查及前人研究基础上,选择控制该区域滑坡发育的19个要素作为影响因子; 经过主成分分析和独立性检验得到该区域对滑坡形成贡献最大的6个因子:高程、坡度、坡向、岩性、断裂距离和人口密度; 分别使用二元逻辑回归模型（LR）和支持向量机模型（SVM）对该区域进行滑坡敏感性评价; 最后,采用ROC曲线对模型精度进行验证。研究结果表明,两模型各能将38.76%、14.48%、9.40%、11.28%、26.07%和13.49%、21.61%、8.17%、26.70%、30.04%的边坡单元分别预测为极高危险区、高危险区、中度危险区、低危险区和极低危险区; 精度验证结果表明两种模型均能有效地进行该区域滑坡敏感性评价,并且支持向量机模型具有更好的分类能力、预测精度和稳定性。     

Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy

The aim of this study is the application of support vector machines (SVM) to landslide susceptibility mapping. SVM are a set of machine learning methods in which model capacity matches data complexity. The research is based on a conceptual framework targeted to apply and test all the procedural steps for landslide susceptibility modeling from model selection, to investigation of predictive variables, from empirical cross-validation of results, to analysis of predicted patterns. SVM were successfully applied and the final susceptibility map was interpreted via success and prediction rate curves and receiver operating characteristic (ROC) curves, to support the modeling results and assess the robustness of the model. SVM appeared to be very specific learners, able to discriminate between the informative input and random noise. About 78% of occurrences was identified within the 20% of the most susceptible study area for the cross-validation set. Then the final susceptibility map was compared with other maps, addressed by different statistical approaches, commonly used in susceptibility mapping, such as logistic regression, linear discriminant analysis, and naive Bayes classifier. The SVM procedure was found feasible and able to outperform other techniques in terms of accuracy and generalization capacity. The over-performance of SVM against the other techniques was around 18% for the cross-validation set, considering the 20% of the most susceptible area. Moreover, by analyzing receiver operating characteristic (ROC) curves, SVM appeared to be less prone to false positives than the other models. The study was applied in the Staffora river basin (Lombardy, Northern Italy), an area of about 275 km² characterized by a very high density of landslides, mainly superficial slope failures triggered by intense rainfall events.     

Landslide susceptibility assessment using SVM machine learning algorithm

This paper introduces the current machine learning approach to solving spatial modeling problems in the domain of landslide susceptibility assessment. The latter is introduced as a classification problem, having multiple (geological, morphological, environmental etc.) attributes and one referent landslide inventory map from which to devise the classification rules. Three different machine learning algorithms were compared: Support Vector Machines, Decision Trees and Logistic Regression. A specific area of the Fruška Gora Mountain (Serbia) was selected to perform the entire modeling procedure, from attribute and referent data preparation/processing, through the classifiers' implementation to the evaluation, carried out in terms of the model's performance and agreement with the referent data. The experiments showed that Support Vector Machines outperformed the other proposed methods, and hence this algorithm was selected as the model of choice to be compared with a common knowledge-driven method – the Analytical Hierarchy Process – to create a landslide susceptibility map of the relevant area. The SVM classifier outperformed the AHP approach in all evaluation metrics (κ index, area under ROC curve and false positive rate in stable ground class).     

基于粗糙集的支持向量机滑坡易发性评价

区域滑坡易发性评价对灾害中长期预测预报具有重要意义。以三峡库区秭归至巴东段为研究区,利用粗糙集理论对20个初始评价因子进行属性约简,去掉冗余或干扰信息,得到13个核心评价因子,并以此作为支持向量机的输入特征集,构建支持向量机模型,实现滑坡易发性评价。在易发性分区图中高易发区占8.2%,主要分布在童庄河右岸、归州河沿岸、青干河左岸、树坪至范家坪长江右岸、牛口到东壤口长江左岸和巴东附近;不易发区占 52.7%,主要分布于店子湾至巴东旧城以及远离长江水系及植被覆盖度高的区域。通过验证与分析,粗糙集-支持向量机模型在高中易发区中的预测精度为85.6%,其预测能力优于支持向量机模型;与野外调查对比,预测结果与实际情况吻合较好。研究表明,应用粗糙集和支持向量机相结合进行滑坡易发性评价具有预测能力强、计算效率高等优点。     

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.     

A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area

The objective of this study is to explore and compare the least square support vector machine (LSSVM) and multiclass alternating decision tree (MADT) techniques for the spatial prediction of landslides. The Luc Yen district in Yen Bai province (Vietnam) has been selected as a case study. LSSVM and MADT are effective machine learning techniques of classification applied in other fields but not in the field of landslide hazard assessment. For this, Landslide inventory map was first constructed with 95 landslide locations identified from aerial photos and verified from field investigations. These landslide locations were then divided randomly into two parts for training (70 % locations) and validation (30 % locations) processes. Secondly, landslide affecting factors such as slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to faults, distance to rivers, and rainfall were selected and applied for landslide susceptibility assessment. Subsequently, the LSSVM and MADT models were built to assess the landslide susceptibility in the study area using training dataset. Finally, receiver operating characteristic curve and statistical index-based evaluations techniques were employed to validate the predictive capability of these models. As a result, both the LSSVM and MADT models have high performance for spatial prediction of landslides in the study area. Out of these, the MADT model (AUC = 0.853) outperforms the LSSVM model (AUC = 0.803). From the landslide study of Luc Yen district in Yen Bai province (Vietnam), it can be conclude that the LSSVM and MADT models can be applied in other areas of world also for and spatial prediction. Landslide susceptibility maps obtained from this study may be helpful in planning, decision making for natural hazard management of the areas susceptible to landslide hazards.     

基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价

准确的滑坡易发性评价结果是滑坡风险评价的重要基础.为提升滑坡易发性评价精度，以三峡库区龙驹坝为例，选取坡度等10个因子构建滑坡易发性评价指标体系，应用频率比方法定量分析各指标与滑坡发育的关系.在此基础上，随机选取70%/30%的滑坡数据作为训练/测试样本，应用径向基神经网络和Adaboost集成学习耦合模型(RBNN-Adaboost)，径向基神经网络和逻辑回归模型分别开展易发性评价.结果显示:水系距离、坡度等是滑坡发育的主控因素；RBNN-Adaboost耦合模型的预测精度最高(0.820)，优于RBNN模型和LR模型的0.781和0.748.Adaboost集成算法能进一步提升模型的预测性能，所提出的耦合模型结合了两者的优点，具有更强的预测能力，是一种可靠的滑坡易发性评价模型.      

A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)

This study proposed a hybrid modeling approach using two methods, support vector machines and random subspace, to create a novel model named random subspace-based support vector machines (RSSVM) for assessing landslide susceptibility. The newly developed model was then tested in the Wuning area, China, to produce a landslide susceptibility map. With the purpose of achieving the objective of the study, a spatial dataset was initially constructed that includes a landslide inventory map consisting of 445 landslide regions. Then, various landslide-influencing factors were defined, including slope angle, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index, land use, rainfall, distance to roads, distance to rivers, and distance to faults. Next, the result of the RSSVM model was validated using statistical index-based evaluations and the receiver operating characteristic curve approach. Then, to evaluate the performance of the suggested RSSVM model, a comparison analysis was performed to other existing approaches such as artificial neural network, Naïve Bayes (NB) and support vector machine (SVM). In general, the performance of the RSSVM model was better than the other models for spatial prediction of landslide susceptibility. The AUC results of the applied models are as follows: RSSVM (AUC = 0.857), followed by MLP (AUC = 0.823), SVM (AUC = 0.814) and NB (AUC = 0.783). The present study indicates that RSSVM can be used for landslide susceptibility evaluation, and the results are very useful for local governments and people living in the Wuning area.     

A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.     

Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.