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1.
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
2.
3.
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
4.
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
5.
Ahmed Mohamed Youssef Hamid Reza Pourghasemi Zohre Sadat Pourtaghi Mohamed M. Al-Katheeri 《Landslides》2016,13(5):839-856
The purpose of the current study is to produce landslide susceptibility maps using different data mining models. Four modeling techniques, namely random forest (RF), boosted regression tree (BRT), classification and regression tree (CART), and general linear (GLM) are used, and their results are compared for landslides susceptibility mapping at the Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslide locations were identified and mapped from the interpretation of different data types, including high-resolution satellite images, topographic maps, historical records, and extensive field surveys. In total, 125 landslide locations were mapped using ArcGIS 10.2, and the locations were divided into two groups; training (70 %) and validating (25 %), respectively. Eleven layers of landslide-conditioning factors were prepared, including slope aspect, altitude, distance from faults, lithology, plan curvature, profile curvature, rainfall, distance from streams, distance from roads, slope angle, and land use. The relationships between the landslide-conditioning factors and the landslide inventory map were calculated using the mentioned 32 models (RF, BRT, CART, and generalized additive (GAM)). The models’ results were compared with landslide locations, which were not used during the models’ training. The receiver operating characteristics (ROC), including the area under the curve (AUC), was used to assess the accuracy of the models. The success (training data) and prediction (validation data) rate curves were calculated. The results showed that the AUC for success rates are 0.783 (78.3 %), 0.958 (95.8 %), 0.816 (81.6 %), and 0.821 (82.1 %) for RF, BRT, CART, and GLM models, respectively. The prediction rates are 0.812 (81.2 %), 0.856 (85.6 %), 0.862 (86.2 %), and 0.769 (76.9 %) for RF, BRT, CART, and GLM models, respectively. Subsequently, landslide susceptibility maps were divided into four classes, including low, moderate, high, and very high susceptibility. The results revealed that the RF, BRT, CART, and GLM models produced reasonable accuracy in landslide susceptibility mapping. The outcome maps would be useful for general planned development activities in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection. 相似文献
6.
The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks 总被引：4，自引：2，他引：2
Işık Yilmaz 《Environmental Earth Sciences》2010,60(3):505-519
This study presented herein compares the effect of the sampling strategies by means of landslide inventory on the landslide
susceptibility mapping. The conditional probability (CP) and artificial neural networks (ANN) models were applied in Sebinkarahisar
(Giresun–Turkey). Digital elevation model was first constructed using a geographical information system software and parameter
maps affecting the slope stability such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect,
topographic wetness index, stream power index and normalized difference vegetation index were considered. In the last stage
of the analyses, landslide susceptibility maps were produced applying different sampling strategies such as; scarp, seed cell
and point. The maps elaborated were then compared by means of their validations. Scarp sampling strategy gave the best results
than the point, whereas the scarp and seed cell methods can be evaluated relatively similar. Comparison of the landslide susceptibility
maps with known landslide locations indicated that the higher accuracy was obtained for ANN model using the scarp sampling
strategy. The results obtained in this study also showed that the CP model can be used as a simple tool in assessment of the
landslide susceptibility, because input process, calculations and output process are very simple and can be readily understood. 相似文献
7.
Subeg Man Bijukchhen Prabin Kayastha Megh Raj Dhital 《Arabian Journal of Geosciences》2013,6(8):2727-2743
Landslides are one of the major natural disasters that occur in the Himalayan range with recurring frequency, causing enormous loss of life and property every year. Preparation of landslide inventory maps and landslide susceptibility zonation maps are the important tasks to be taken into account initially for safe mitigation measures. The present paper focuses on landslide susceptibility maps of the Ghurmi–Dhad Khola area, east Nepal, using Geographic Information System. For this purpose, the landslide susceptibility maps are prepared by using the heuristic and bivariate statistical methods. The parameters considered for the study are slope angle, slope aspect, elevation, distance from drainage, geology, land cover, rock and soil type, and distance from faults and folds. The landslide susceptibility zonation map produced from the heuristic method shows that 42.59 % of the observed landslide falls under the very high susceptible zone and 33.00 % under the high susceptible zone. Likewise, the landslide susceptibility zonation map produced from the bivariate method depicts that 44.19 % of the observed landslide falls under the very high susceptible zone and 31.59 % under the high susceptible zone. Both the landslide susceptibility zonation maps are identical, and success rates of both the maps are above 80 %. While comparing the landslide susceptibility maps obtained from two different methods, about 78 % of the study area falls in the identical susceptible zones. Special attention should be taken into consideration for the construction works in the areas which have been spatially agreed as very high and high susceptible zones from both techniques. Moreover, these maps can be used for slope management, land use planning, disaster management planning, etc., by the concerned authorities. 相似文献
8.
Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran 总被引：41，自引：8，他引：33
The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose. 相似文献
9.
基于GIS与ANN模型的地震滑坡易发性区划 总被引：1，自引：0，他引：1
基于遥感数据、地理信息系统(GIS)技术和人工神经网络(ANN)模型,开展地震滑坡易发性区划研究.2010年4月14日玉树地震后,基于航片与卫星影像目视解译,并辅以野外调查的方法,在地震区圈定了2036处地震诱发滑坡.选择高程、坡度、坡向、斜坡曲率、坡位、与水系距离、地层岩性、与断裂距离、与公路距离、归一化植被指数(NDVI)、与同震地表破裂距离、地震动峰值加速度(PGA)共12个因子作为地震滑坡易发性评价因子.这些因子均是应用GIS技术与遥感影像处理技术,基于地形数据、地质数据、遥感数据得到.训练样本中的滑动样本有两组,一组是滑坡区整个单滑坡体的质心位置,另一组是滑坡滑源区滑前的坡体高程最高的位置.应用这12个影响因子,分别采用这两组评价样本,基于ANN模型建立地震滑坡易发性索引图,基于GIS工具建立地震滑坡易发性分级图.分别应用训练样本中滑坡分布的点数据去检验各自的结果正确率,正确率分别为81.53％与81.29％,表明ANN模型是一种高效科学的地震滑坡易发性区划模型. 相似文献
10.
Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya 总被引：11，自引：5，他引：6
Chandra Prakash Poudyal Chandong Chang Hyun-Joo Oh Saro Lee 《Environmental Earth Sciences》2010,61(5):1049-1064
This study considers landslide susceptibility mapping by means of frequency ratio and artificial neural network approaches
using geographic information system (GIS) techniques as a basic analysis tool. The selected study area was that of the Panchthar
district, Nepal. GIS was used for the management and manipulation of spatial data. Landslide locations were identified from
field survey and aerial photographic interpretation was used for location of lineaments. Ten factors in total are related
to the occurrence of landslides. Based on the same set of factors, landslide susceptibility maps were produced from frequency
ratio and neural network models, and were then compared and evaluated. The weights of each factor were determined using the
back-propagation training method. Landslide susceptibility maps were produced from frequency ratio and neural network models,
and they were then compared by means of their checking. The landslide location data were used for checking the results with
the landslide susceptibility maps. The accuracy of the landslide susceptibility maps produced by the frequency ratio and neural
networks is 82.21 and 78.25%, respectively. 相似文献
11.
Vahid Nourani Biswajeet Pradhan Hamid Ghaffari Seyed Saber Sharifi 《Natural Hazards》2014,71(1):523-547
Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices. 相似文献
12.
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
13.
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. 相似文献
14.
《地学前缘(英文版)》2021,12(3)
Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities.It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods.Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters.In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace(RS) coupled with Artificial Neural Network(ANN), Random Forest(RF), and Support Vector Machine(SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh.The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment.The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors.For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve(ROC) were employed.The value of the Area Under the Curve(AUC) of ROC was above 0.80 for all models.For flood susceptibility modelling, the Dagging model performs superior, followed by RF,the ANN, the SVM, and the RS, then the several benchmark models.The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage. 相似文献
15.
Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia 总被引：20，自引：5，他引：15
Biswajeet Pradhan 《Environmental Earth Sciences》2011,63(2):329-349
Landslides are one of the most frequent and common natural hazards in Malaysia. Preparation of landslide susceptibility maps
is one of the first and most important steps in the landslide hazard mitigation. However, due to complex nature of landslides,
producing a reliable susceptibility map is not easy. For this reason, a number of different approaches have been used, including
direct and indirect heuristic approaches, deterministic, probabilistic, statistical, and data mining approaches. Moreover,
these landslides can be systematically assessed and mapped through a traditional mapping framework using geoinformation technologies.
Since the early 1990s, several mathematical models have been developed and applied to landslide hazard mapping using geographic
information system (GIS). Among various approaches, fuzzy logic relation for mapping landslide susceptibility is one of the
techniques that allows to describe the role of each predisposing factor (landslide-conditioning parameters) and their optimal
combination. This paper presents a new attempt at landslide susceptibility mapping using fuzzy logic relations and their cross
application of membership values to three study areas in Malaysia using a GIS. The possibility of capturing the judgment and
the modeling of conditioning factors are the main advantages of using fuzzy logic. These models are capable to capture the
conditioning factors directly affecting the landslides and also the inter-relationship among them. In the first stage of the
study, a landslide inventory was complied for each of the three study areas using both field surveys and airphoto studies.
Using total 12 topographic and lithological variables, landslide susceptibility models were developed using the fuzzy logic
approach. Then the landslide inventory and the parameter maps were analyzed together using the fuzzy relations and the landslide
susceptibility maps produced. Finally, the prediction performance of the susceptibility maps was checked by considering field-verified
landslide locations in the studied areas. Further, the susceptibility maps were validated using the receiver-operating characteristics
(ROC) success rate curves. The ROC curve technique is based on plotting model sensitivity—true positive fraction values calculated
for different threshold values versus model specificity—true negative fraction values on a graph. The ROC curves were calculated
for the landslide susceptibility maps obtained from the application and cross application of fuzzy logic relations. Qualitatively,
the produced landslide susceptibility maps showed greater than 82% landslide susceptibility in all nine cases. The results
indicated that, when compared with the landslide susceptibility maps, the landslides identified in the study areas were found
to be located in the very high and high susceptibility zones. This shows that as far as the performance of the fuzzy logic
relation approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones
of relative susceptibility. 相似文献
16.
Dieu Tien Bui Tran Anh Tuan Harald Klempe Biswajeet Pradhan Inge Revhaug 《Landslides》2016,13(2):361-378
Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping. 相似文献
17.
Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy 总被引：4，自引：0，他引：4
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. 相似文献
18.
《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
19.
数理统计和机器学习模型如支持向量机(support vector machine,SVM)等,在区域滑坡敏感性评价中得到广泛的应用.但这些模型的建模过程往往较复杂,如在对机器学习进行训练和测试时难以选取合理的非滑坡栅格单元,而且有较多的模型参数需要确定.为提高滑坡敏感性评价建模的效率和精度,提出基于灰色关联度的敏感性评价模型.灰色关联度模型能有效计算各比较样本与参考样本之间的定量的关联度,具有建模过程简洁和评价精度高的优点,该模型目前在区域滑坡敏感性评价中的应用还没有引起研究人员的足够关注且有待进一步拓展.拟将灰色关联度模型用于浙江省飞云江流域南田—雅梅图幅(南田地区)的滑坡敏感性评价,并将得到的评价结果与SVM模型的敏感性评价结果作对比分析.结果显示,灰色关联度模型在高和极高敏感区的滑坡预测精度优于SVM模型,而在中等敏感区的滑坡预测精度略低于SVM模型;整体而言,灰色关联度模型对整个南田地区滑坡敏感性分布的预测精度略高于SVM模型.对两个模型建模过程的对比结果显示,灰色关联度模型建模较简单,具有比SVM模型更高的建模效率,为滑坡敏感性评价提供了一种新思路. 相似文献
20.
The aim of this study is to make a comparison of the performances of two machine-learning algorithms that support vector machine (SVM) and random forest (RF) for landslide susceptibility mapping. The study makes use of a sampling strategy called two-level random sampling (2LRS). During landslide susceptibility mapping, training and testing samples must be collected from different landslide seed cells, which are then put through a fully independent sampling using the 2LRS algorithm. This approach requires fewer samples for the improvement of the computation time of both machine-learning classifications. The proposed approach was tested in the Alakir catchment area (Western Antalya, Turkey) which features numerous active deep-seated rotational landslides. In order to compare the performance of the machine-learning algorithms, three random sets were generated for SVM and three random sets generated for 10, 100, 1000 and 10,000-tree size RF. A total of 15 models were generated for comparison, and their spatial performances were performed by the area under the receiver-operating characteristic curves, which ranged between 0.82 and 0.87. The highest and lowest performances were recorded from two models in SVM and two models from the 1000-tree and 10,000-tree sized RF, respectively. These results were confirmed the landslide happened just after producing the susceptibility maps in the field. 相似文献