逻辑回归和信息量模型在山区滑坡易发性评价中适宜性的分析

以万山区为例,在区域滑坡孕灾条件的基础上,筛选工程地质岩组、斜坡结构、平均坡度、地貌、距构造距离及距河流距离共6个易发条件因子,选取逻辑回归模型和信息量模型对山区滑坡进行易发性评价。结果显示逻辑回归模型中中高易发区面积占比分别为1578%和1970%,82%的地质灾害点落在该区域内;信息量模型中中高易发区面积占比为1241%、2519%,包含了区域88%的滑坡灾害点。最后通过实际发生的灾害点在各易发区的分布情况进行检验,逻辑回归模型中灾害点落在高易发区的比例远小于信息量模型,且高易发等级中灾害点实际发生的比值较小,说明针对山区区域滑坡地质灾害易发性评价结果预测上,信息量模型的评价结果更为客观准确。     

基于不同耦合模型的县域滑坡易发性评价对比分析

为有效预测县域滑坡发生的空间概率,探索不同统计学耦合模型滑坡易发性定量评价结果的合理性和精度,以四川省普格县为研究对象。选取坡度、坡向、高程、工程地质岩组、断层和斜坡结构等6项孕灾因子作为评价指标体系,基于信息量模型（I）、确定性系数模型（CF）、证据权模型（WF）、频率比模型（FR）分别与逻辑回归模型（LR）耦合开展滑坡易发性评价。结果表明:各耦合模型评价结果和易发程度区划均是合理的,极高易发区主要分布于则木河、黑水河河谷两侧斜坡带,面积介于129.04～183.43 km2（占比6.77%～9.62%）,各模型评价精度依次为WF-LR模型（AUC=0.869）>I-LR模型（AUC=0.868）>CF-LR模型（AUC=0.866）>NFR-LR模型（AUC=0.858）。研究成果可为川西南山区县域滑坡易发性定量评估提供重要参考。     

不同组合模型区域滑坡易发性及精度分析

为探索区域滑坡易发性评价模型的适用性和评价结果的合理性,以滑坡灾害高发的白龙江流域为研究区,首先选取坡度、地形起伏度、距断层距离、地层岩性、流域沟壑密度、植被指数等6项影响滑坡发生的孕灾因子作为易发性的评价指标,以研究区2 093处滑坡灾害点为样本数据,依据各指标条件下的信息量值、确定性系数值和证据权重值曲线突变规律,并结合滑坡面积及分级面积频率比曲线作为等级划分的临界值来确定因子分级状态;其次,基于指标因子状态分级和相关性分析结果,采用信息量法、确定性系数法、证据权法分别与逻辑回归组合的3种模型开展区域滑坡灾害易发性评价,并从模型结果、适用性和精度等方面采用多手段对3种组合模型进行比较和讨论。研究结果表明:在区域滑坡易发性评价方面,3组模型均表现较为理想,信息量和逻辑回归组合模型的预测精度为94.6%,其预测精度和准确性优于其他2种组合模型。笔者以白龙江流域中游及其岷江支流段为例,开展滑坡灾害易发性评价模型适用性、评价结果分析以及预测精度评价对比和研究等,成果可为该区地质灾害防灾减灾和国土空间用途管制规划决策提供参考。     

基于GIS与WOE-BP模型的滑坡易发性评价

区域滑坡易发性研究对地质灾害风险管理具有重要意义.以往研究中,将多元统计模型与机器学习方法相结合用于滑坡易发性评价的研究较少.以三峡库区万州区为例,首先选取9种指标因子（坡度、坡向、剖面曲率、地表纹理、地层岩性、斜坡结构、地质构造、水系分布及土地利用类型）作为滑坡易发性评价指标.基于证据权模型（weights of evidence,WOE）计算得到的对比度和滑坡面积比与分级面积比的相对大小,对各指标因子进行状态分级;再利用粒子群法优化的BP神经网络模型（PSO-BP）得到各指标因子权重.综合两种模型确定的状态分级权重和指标因子权重（WOE-BP）计算滑坡易发性指数（landslide susceptibility index,LSI）,基于GIS平台得到全区滑坡易发性分区图.结果表明:水系、地层岩性和地质构造是影响万州区滑坡发育的主要指标因子;WOE-BP模型的预测精度为80.8%,优于WOE模型的73.1%和BP神经网络模型的71.6%,可为定量计算指标因子权重和优化滑坡易发性评价提供有效途径.      

基于证据权与Logistic回归模型耦合的滑坡易发性评价

滑坡灾害易发性研究对地质灾害风险管理及减灾防灾有着重要的现实意义。目前,多模型耦合的评价方法在国内外应用较为广泛,但将证据权与其他方法相结合用于滑坡易发性评价的研究却较少。鉴于此,本文以浙江省永嘉县为例进行滑坡易发性评价,选取高程等9个因素作为滑坡易发性的评价因子。利用证据权模型计算得到的证据权对比度与分级栅格比、滑坡栅格比进行比较,实现各评价因子状态分级处理;再运用Logistic回归模型算得各评价因子的权重。综合两种模型确定的状态分级权重和评价因子权重,基于GIS的栅格运算功能得到各评价单元的滑坡发生概率,实现研究区滑坡易发性分级区划。研究结果表明,证据权与Logistic回归耦合模型的评价结果的合理性与精确度均优于两种单一模型;且极高易发区和高易发区主要分布在水系延展区、断层密集区、岩组软弱区。研究结果对滑坡灾害风险管理及城市防灾规划具有一定的参考价值。     

基于斜坡单元的山区城镇滑坡灾害易发性评价：以康定为例

山区地质灾害易发性评价对城镇地质灾害风险管理具有重要意义。本文以康定市为例,以斜坡单元为最小评价单元,选取高程、坡度、坡向、曲率、工程地质岩组、距道路距离、距断裂距离、距水系距离和斜坡结构等9个滑坡影响因子,根据各因子滑坡面积比曲线与证据权值曲线的突变点,划分滑坡影响因子二级状态,并对各影响因子进行相关性分析,剔除相关性较高的距道路距离因子,在此基础上,采用证据权模型进行滑坡易发性评价。对已有治理工程的斜坡单元,本文尝试利用折减系数法对其易发性进行进一步评价。结合现场调查,将研究区滑坡易发性程度划分为：极高易发、高易发、中等易发、低易发。评价结果表明,自然工况下极高易发区主要位于康定市炉城镇以及研究区北侧二道桥村一带,高易发区主要位于雅拉河、折多河与瓦斯沟河谷两侧,对治理工程所在的斜坡单元进行折减后,极高易发区面积由11.21%降至8.42%,滑坡比率由4.03降低至2.3,研究结果符合实际情况,模型精度达77.8%。评价结果较好地反映了康定市区的滑坡易发性分布情况,可为城镇精细化评价提供一定的参考依据。     

基于信息量、逻辑回归及其耦合模型的滑坡易发性评估研究:以青海沙塘川流域为例

滑坡易发性定量评估是预测滑坡发生空间概率的重要手段,基于统计分析原理的评估方法目前在国内外应用最为广泛,且不同评估方法的对比研究逐渐成为热点。以青海沙塘川流域黄土梁峁区为例,剖析了信息量模型和逻辑回归模型在滑坡易发性评估中的优越性和局限性,并探索提出基于二者的耦合模型。考虑坡度、坡向、起伏度、岩性、与干流距离、与支流距离和植被指数等7个影响因素,对比分析了基于信息量、逻辑回归及二者耦合模型的滑坡易发性评估的技术流程及结果。3种模型的成功率分别为:耦合模型成功率(78. 9%)>信息量模型成功率(71. 8%)>逻辑回归模型成功率(70. 8%)。在沙塘川流域黄土滑坡的易发性评估中,信息量和逻辑回归模型的表现基本相当,但信息量-逻辑回归耦合模型的成功率明显提升。该研究结果可为黄土高原区滑坡易发性定量评估提供借鉴。     

北京山区突发性地质灾害易发性评价

北京山区地质环境条件复杂,发育大量突发地质灾害隐患,既直接威胁山区村庄、道路、景区的人员及设施的安全,又会对城镇的规划建设构成威胁。通过开展地质灾害易发性评价工作,划分出地质灾害易发区,以评价结果指导城镇建设规划,减轻地质灾害的威胁,这是一项十分重要的工作。文章在阐述北京山区崩塌、滑坡及泥石流突发地质灾害发育情况的基础上,选取了坡度、起伏度、工程地质岩组、地质构造、地貌类型及降水等6个影响因子,采用综合信息量模型方法,分别对北京山区斜坡类灾害（崩塌、滑坡）和泥石流灾害的易发性进行评价,并根据“就高不就低”的原则,叠加各灾种的易发性评价结果划分出北京山区突发地质灾害易发性分区图,为城镇建设适宜性评价、编制国土空间规划及完善空间治理提供科学的依据。     

基于不同模型的赣南地区小型削方滑坡易发性评价对比分析

赣南地区滑坡灾害点多、面广、规模小,具有群发性和突发性的特点,90%以上的滑坡是因人工切坡导致的。为研究赣南地区小型削方滑坡对易发性评价模型的适用性,以赣州市于都县银坑镇为例,基于野外地质调查成果,并利用地理探测器,选取坡度、坡体结构、岩组、断层、道路、植被等6个评价指标,分别选用信息量模型、人工神经网络模型、决策树模型和逻辑回归模型开展易发性评价。结果表明:信息量、人工神经网络、决策树和逻辑回归等模型得到的AUC值分别为0.800、0.708、0.672和0.586,信息量模型所得的易发性结果与研究区滑坡实际分布情况较吻合,高易发区和中易发区滑坡占比近80%。信息量模型较其他三个模型,更适合于赣南地区小型削方滑坡易发性评价,评价结果对该地区地质灾害易发性评价模型选取提供了参考与借鉴。     

罗平县崩滑易发性评价模型对比研究

准确的地质灾害易发性分区评价结果,可为建立地质灾害监测预警系统及处理机制提供参考。依据崩滑地质灾害形成条件选取10个评价因子构建评价指标体系,基于共线性诊断和相关性分析检验评价因子以保证其相互独立。分别采用信息量模型(ICM)、归一化频率比模型(NFR)以及与逻辑回归(LR)耦合的信息量–逻辑回归(ICM-LR)耦合模型和归一化频率比–逻辑回归(NFR-LR)耦合模型对罗平县崩滑地质灾害进行易发性评价,并将评价模型结果划分为低、中、高和极高4个等级。采用ROC曲线对评价结果进行精度检验,其AUC值分别为0.820、0.796、0.882和0.840。得出ICM-LR模型的精度最高,且极高易发区主要分布在砂岩、碳酸盐岩组区域和水系延展区域。其低、中、高和极高的面积(分级比)分别为771.1 km²(25.55%)、836.6 km²(27.73%)、864.36 km²(28.64%)和545.94 km²(18.08%)。易发性分区结果与研究区崩滑地质灾害分布情况相符合,可为快速建立评价指标体系和区...     

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 by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances

The current research presents a detailed landslide susceptibility mapping study by binary logistic regression, analytical hierarchy process, and statistical index models and an assessment of their performances. The study area covers the north of Tehran metropolitan, Iran. When conducting the study, in the first stage, a landslide inventory map with a total of 528 landslide locations was compiled from various sources such as aerial photographs, satellite images, and field surveys. Then, the landslide inventory was randomly split into a testing dataset 70 % (370 landslide locations) for training the models, and the remaining 30 % (158 landslides locations) was used for validation purpose. Twelve landslide conditioning factors such as slope degree, slope aspect, altitude, plan curvature, normalized difference vegetation index, land use, lithology, distance from rivers, distance from roads, distance from faults, stream power index, and slope-length were considered during the present study. Subsequently, landslide susceptibility maps were produced using binary logistic regression (BLR), analytical hierarchy process (AHP), and statistical index (SI) models in ArcGIS. The validation dataset, which was not used in the modeling process, was considered to validate the landslide susceptibility maps using the receiver operating characteristic curves and frequency ratio plot. The validation results showed that the area under the curve (AUC) for three mentioned models vary from 0.7570 to 0.8520 $ ({\text{AUC}}_{\text{AHP}} = 75.70\;\% ,\;{\text{AUC}}_{\text{SI}} = 80.37\;\% ,\;{\text{and}}\;{\text{AUC}}_{\text{BLR}} = 85.20\;\% ) $ ( AUC AHP = 75.70 % , AUC SI = 80.37 % , and AUC BLR = 85.20 % ) . Also, plot of the frequency ratio for the four landslide susceptibility classes of the three landslide susceptibility models was validated our results. Hence, it is concluded that the binary logistic regression model employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of study area. Meanwhile, the results obtained in this study also showed that the statistical index model can be used as a simple tool in the assessment of landslide susceptibility when a sufficient number of data are obtained.     

Validation and evaluation of two multivariate statistical models for predictive shallow landslide susceptibility mapping of the Eastern Pyrenees (Spain)

This paper deals with the quality of two multivariate statistical models based on the Geographical Information System for shallow landslide susceptibility assessment in a test area at La Pobla de Lillet (Eastern Pyrenees, Spain). The quality, which was guaranteed by a rigorous methodology based on a suitable diagnosis, validation, and evaluation of the models, ensured a reliable contrast of the final susceptibility maps. This enables us to transfer the best results to the end user. Landslide susceptibility models were carried out by logistic regression and discriminant analysis of the significant conditioning factors related to the characteristics of the slope and the upslope contributing area captured from the digital elevation model and landslide distribution. The explanatory variables were tested (KS test, principal components and one-way and T-test) to select the most statistically significant ones before being introduced into the logistic and discriminant analyses. Accuracy statistics and the receiver operating characteristic curve used for diagnosis and validation showed similar prediction skills and a good fit to the data with more than 85% of unfailed cells properly classified for the two models. The evaluation of the study area and the correlation function (R ² = 0.83) between the models revealed that the discriminant model overestimated the susceptibility of the most stable zones with respect to the logistic model. Different methods of producing susceptibility maps showed marked differences in matching the models. Substantial spatial agreement (Kappa = 0.741) between binary maps produced by the standard cut-off value descended moderately (Kappa = 0.540) as a result of superimposing maps with five susceptibility levels defined by landslide percentage. Despite the fact that the two statistical models are similar in assessing susceptibility in the study area, the implications for hazard and risk management can be different because of the conservative nature of the discriminant model.     

四川雅安市雨城区地质灾害预警系统研究

区域地质灾害的敏感性评价与诱发因素评价是区域群发性地质灾害预警预报的基础。文中以四川雅安市雨城区为例,系统介绍了区域地质灾害预警预报的方法。在区域地质灾害敏感性评价及区域降雨监测的基础上,研究了降雨诱发区域群发性地质灾害的规律,得出了地质灾害降雨阈值。研究了降雨诱发地质灾害预警预报的方法,建立了大中比例尺的地质灾害预警预报系统,并在四川雅安雨城区开展了系统运行。文中论述的方法可以用于县(市)级的地质灾害预警预报工作。模拟运行及实际运行效果表明,本方法效果较好,能够在类似区域进行推广。     

A comparative study on the landslide susceptibility mapping using logistic regression and statistical index models

The logistic regression and statistical index models are applied and verified for landslide susceptibility mapping in Daguan County, Yunnan Province, China, by means of the geographic information system (GIS). A detailed landslide inventory map was prepared by literatures, aerial photographs, and supported by field works. Fifteen landslide-conditioning factors were considered: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, STI, SPI, and TWI were derived from digital elevation model; NDVI was extracted from Landsat ETM7; rainfall was obtained from local rainfall data; distance to faults, distance to roads, and distance to rivers were created from a 1:25,000 scale topographic map; the lithology was extracted from geological map. Using these factors, the landslide susceptibility maps were prepared by LR and SI models. The accuracy of the results was verified by using existing landslide locations. The statistical index model had a predictive rate of 81.02%, which is more accurate prediction in comparison with logistic regression model (80.29%). The models can be used to land-use planning in the study area.     

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.      

基于信息量模型和信息量-逻辑回归模型的海南岛中部山区地质灾害易发性研究

地质灾害易发性评价作为地质灾害风险评价的基础,运用定量化的数学统计原理对地质灾害易发性进行研究能够客观准确地反映地质灾害发生的概率.文章以海南岛地质灾害最为发育的五指山市为例,选择断裂、 岩土体、 坡度、 地形起伏度、 海拔高程变异系数、 归一化植被指数(NDVI)、 降雨量、 水系、 公路、 曲率值为评价指标,依托详...     

Mapping landslide susceptibility with frequency ratio,statistical index,and weights of evidence models: a case study in northern Iran

The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.