共查询到18条相似文献，搜索用时 140 毫秒
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拟深入探讨滑坡与其环境因子间的非线性联接计算以及不同数据驱动模型等因素,对滑坡易发性预测建模不确定性的影响规律.以江西省瑞金市为例共获取370处滑坡和10种环境因子,通过概率统计（probability statistics,PS）、频率比（frequency ratio,FR）、信息量（information value,Ⅳ）、熵指数（index of entropy,IOE）和证据权（weight of evidence,WOE）等5种联接方法分别耦合逻辑回归（logistic regression,LR）、BP神经网络（BP neural networks,BPNN）、支持向量机（support vector machines,SVM）和随机森林（random forest,RF）模型共构建出20种耦合模型,同时构建无联接方法直接将原始数据作为输入变量的4种单独LR、BPNN、SVM和RF模型,预测出总计24种工况下的滑坡易发性;最后分别使用ROC曲线、均值、标准差和差异显著性等指标分析上述24种工况下易发性结果的不确定性.结果表明:（1）基于WOE的耦合模型预测滑坡易发性的平均精度最高且不确定性较低,基于PS的耦合模型预测精度最低且不确定性最高,基于FR、Ⅳ和IOE的耦合模型介于两者之间;（2）单独数据驱动模型易发性预测精度略低于耦合模型,且未能计算出环境因子各子区间对滑坡发育的影响规律,但其建模效率高于耦合模型;（3）RF模型预测精度最高且不确定性较低,其次分别为SVM、BPNN和LR模型.总之WOE是更优秀的联接法且RF模型预测性能最优,WOE-RF模型预测的滑坡易发性不确定性较低且更符合实际滑坡概率分布特征. 相似文献
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目前基于GIS的泥石流易发性（简称DFS）评价模型中,统计类型模型的因子须保证独立性,且权重受区间划分控制;线性机器学习难以处理非线性问题、而常用非线性模型调试效率低.鉴于随机森林（RF）能有效克服常用模型的诸多不足,且在DFS评价中的应用极少,首先展开基于RF的DFS评价,采用线性、RBF支持向量机、二次判别分析、RF等经贝叶斯优化的模型和26种泥石流影响因子;然后,分别以RF的相对权重排序和蒙特卡洛方法研究因子组合和建模样本变化下DFS评价的可靠性.结果表明:RF不易发和较易发区中有21个因子可指示泥石流孕育环境差异;RF的相对权重排序能有效确定易发模型的局部最优因子组合;随机样本划分导致的评价不确定性在中易发区最大,应通过提高建模样本比例和改善模型降低;RF的预测能力指标AUC为0.86、全局预测精度为0.79、F1分数为0.66、brier分数为0.14,以及它们的可靠度最优,可作为DFS定量评估的优先选择. 相似文献
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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... 相似文献
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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。 相似文献
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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(Sei)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SeV值最高,分别为0.867、0.94、9.21;BP神经网络模型次之,其SeV值分别为0.829、0.90、9.14;SVM最低,其SeV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... 相似文献
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本文以湖北省远安县为研究区,利用采集的资料,提取出了与滑坡发生相关的8类指标因子:高程、坡度、坡向、地层岩性、斜坡结构、断层、水系、公路。针对连续型致灾因子,选取定性等间距划分和频率比法划分得到两类指标因子体系,分别带入人工神经网络模型和随机森林模型,绘制得研究区易发性评价区划图。最后,利用ROC曲线图对4个模型的精确性进行分析,得到ANN模型的成功率和预测率分别为0.899和0.901,FR-ANN模型的成功率和预测率0.934和0.935; RF模型的成功率和预测率分别为0.886和0.886,FR-RF模型的成功率和预测率分别为0.928和0.929。以上说明,无论对于人工神经网络还是随机森林模型,基于频率比法的因子分级均表现出了更高的精确性。 更多还原 相似文献
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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. 相似文献
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《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
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Işık Yilmaz 《Environmental Earth Sciences》2010,61(4):821-836
This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability
(CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas,
Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults,
drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized
difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the
last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared
by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained
from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar.
The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS
operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes
are very simple in CP model when compared with the other methods considered in this study. 相似文献
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Soyoung Park Chuluong Choi Byungwoo Kim Jinsoo Kim 《Environmental Earth Sciences》2013,68(5):1443-1464
Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. The areas under the relative operating characteristic (ROC) curves for the FR, AHP, LR, and ANN LSI maps were 0.794, 0.789, 0.794, and 0.806, respectively. Thus, the LSI maps developed by all the models had similar accuracy. A cross-tabulation analysis of landslide occurrence against non-occurrence areas showed generally similar overall accuracies of 65.27, 64.35, 65.51, and 68.47 % for the FR, AHP, LR, and ANN models, respectively. A correlation analysis between the models demonstrated that the LR and ANN models had the highest correlation (0.829), whereas the FR and AHP models had the lowest correlation (0.619). 相似文献
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Landslide-related factors were extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, and integrated techniques were developed, applied, and verified for the analysis of landslide susceptibility in Boun, Korea, using a geographic information system (GIS). Digital elevation model (DEM), lineament, normalized difference vegetation index (NDVI), and land-cover factors were extracted from the ASTER images for analysis. Slope, aspect, and curvature were calculated from a DEM topographic database. Using the constructed spatial database, the relationships between the detected landslide locations and six related factors were identified and quantified using frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) models. These relationships were used as factor ratings in an overlay analysis to create landslide susceptibility indices and maps. Three landslide susceptibility maps were then combined and applied as new input factors in the FR, LR, and ANN models to make improved susceptibility maps. All of the susceptibility maps were verified by comparison with known landslide locations not used for training the models. The combined landslide susceptibility maps created using three landslide-related input factors showed improved accuracy (87.00% in FR, 88.21% in LR, and 86.51% in ANN models) compared to the individual landslide susceptibility maps (84.34% in FR, 85.40% in LR, and 74.29% in ANN models) generated using the six factors from the ASTER images. 相似文献
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Statistical models are one of the most preferred methods among many landslide susceptibility assessment methods. As landslide
occurrences and influencing factors have spatial variations, global models like neural network or logistic regression (LR)
ignore spatial dependence or autocorrelation characteristics of data between the observations in susceptibility assessment.
However, to assess the probability of landslide within a specified period of time and within a given area, it is important
to understand the spatial correlation between landslide occurrences and influencing factors. By including these relations,
the predictive ability of the developed model increases. In this respect, spatial regression (SR) and geographically weighted
regression (GWR) techniques, which consider spatial variability in the parameters, are proposed in this study for landslide
hazard assessment to provide better realistic representations of landslide susceptibility. The proposed model was implemented
to a case study area from More and Romsdal region of Norway. Topographic (morphometric) parameters (slope angle, slope aspect,
curvature, plan, and profile curvatures), geological parameters (geological formations, tectonic uplift, and lineaments),
land cover parameter (vegetation coverage), and triggering factor (precipitation) were considered as landslide influencing
factors. These influencing factors together with past rock avalanche inventory in the study region were considered to obtain
landslide susceptibility maps by using SR and LR models. The comparisons of susceptibility maps obtained from SR and LR show
that SR models have higher predictive performance. In addition, the performances of SR and LR models at the local scale were
investigated by finding the differences between GWR and SR and GWR and LR maps. These maps which can be named as comparison
maps help to understand how the models estimate the coefficients at local scale. In this way, the regions where SR and LR
models over or under estimate the landslide hazard potential were identified. 相似文献
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Zhengtuan Xie Guan Chen Xingmin Meng Yi Zhang Liang Qiao Long Tan 《Environmental Earth Sciences》2017,76(8):313
The determining of landslide-prone areas in mountainous terrain is essential for land planning and hazard mitigation. In this paper, a comparative study using three statistical models including weight of evidence model (WoE), logistic regression model (LR) and support vector machine method (SVM) was undertaken in the Zhouqu to Wudu segment in the Bailong River Basin, Southern Gansu, China. Six conditionally independent environmental factors, elevation, slope, aspect, distance from fault, lithology and settlement density, were selected as the explanatory variables that may contribute to landslide occurrence based on principal component analysis (PCA) and Chi-square test. The relation between landslide distributions and these variables was analyzed using the three models and the results then used to calculate the landslide susceptibility (LS). The performance of the models was then evaluated using both the highly accurate deformation signals produced by using the Small Baseline Subset Interferometric Synthetic Aperture Radar technique and Receiver Operating Characteristic (ROC) curve. Results show more deformation points in areas with high and very high LS levels, and also more stable points in areas with low and very low LS levels for the SVM model. In addition, the SVM has larger area under the ROC curve. It indicates that the SVM has better prediction accuracy and classified ability. For the interpretability, the WoE derives the class of factors that most contributed to landsliding in the study area, and the LR reveals that factors including elevation, settlement density and distance from fault played major roles in landslide occurrence and distribution, whereas the SVM cannot provide relative weights for the variables. The outperformed SVM could be employed to determine potential landslide zones in the study area. Outcome of this research would provide preliminary basis for general land planning such as choosing new urban areas and infrastructure construction in the future, as well as for landslide hazard mitigation in Bailong River Basin. 相似文献
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Sk Ajim Ali Farhana Parvin Jana Vojteková Romulus Costache Nguyen Thi Thuy Linh Quoc Bao Pham Matej Vojtek Ljubomir Gigović Ateeque Ahmad Mohammad Ali Ghorbani 《地学前缘(英文版)》2021,12(2):857-876
Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Na?ve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Na?ve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%). 相似文献
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In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models. 相似文献