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1.
证据权法在区域滑坡危险性评价中的应用以贵州省为例   总被引：3，自引：0，他引：3  
以GIS为技术平台,采用证据权法对研究区进行了滑坡地质灾害危险性分析。综合分析历史滑坡数据及其环境因素和触发因素,数据源主要有地形图、DEM、地质图,选取地层岩性、构造、高程、坡度、坡向、地形起伏度、道路、水系作为危险性评价因子。首先应用ArcGIS软件对数据源进行处理,提取各个评价因子图层,并对每个图层进行分级、缓冲区分析等处理,建立若干证据层。然后将历史灾害点与评价因子进行空间关联分析,计算每个评价因子等级的权重,最后计算出评价单元的危险性指数,并将危险性分为极高危险区、高危险区、中等危险区、低危险区。采用成功率曲线法对证据权法评价精度进行验证,结果表明本次评价的精度为71%。利用历史滑坡数据对评价结果进行验证,结果显示评价结果与实际情况较为吻合,说明证据权可以客观定量地评价各影响因子对滑坡的影响程度,该方法应用于区域地质灾害危险性评价比较有效。  相似文献   

2.
Landslides cause heavy damage to property and infrastructure, in addition to being responsible for the loss of human lives in many parts of the Turkey. The paper presents GIS-based spatial data analysis for landslide susceptibility mapping in the regions of the Sultan Mountains, West of Akşehir, and central part of Turkey. Landslides occur frequently in the area and seriously affect local living conditions. Therefore, spatial analysis of landslide susceptibility in the Sultan Mountains is important. The relationships between landslide distributions with the 19 landslide affecting parameters were analysed using a Bayesian model. In the study area, 90 landslides were observed. The landslides were randomly subdivided into 80 training landslides and 10 test landslides. A landslide susceptibility map was produced by using the training landslides. The test landslides were used in the accuracy control of the produced landslide susceptibility map. Approximately 9% of the study area was classified as high susceptibility zone. Medium, low and very low susceptibility zones covered 8, 23 and 60% of the study area, respectively. Most of the locations of the observed landslides actually fall into moderate (17.78%) and high (77.78. %) susceptibility zones of the produced landslide susceptibility map. This validates the applicability of proposed methods, approaches and the classification scheme. The high susceptibility zone is along both sides of the Akşehir Fault and at the north-eastern slope of the Sultan Mountains. It was determined that the surface area of the Harlak and Deresenek formations, which have attained lithological characteristics of clayey limestone with a broken and separated base, and where area landslides occur, possesses an elevation of 1,100–1,600 m, a slope gradient of 25°–35° and a slope aspect of 22.5°–157.5° facing slopes.  相似文献   

3.
The main purpose of this paper is to present the use of multi-resource remote sensing data, an incomplete landslide inventory, GIS technique and logistic regression model for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Landslide location polygons were delineated from visual interpretation of aerial photographs, satellite images in high resolutions, and verified by selecting field investigations. Eight factors, including slope angle, slope aspect, elevation, distance from drainages, distance from roads, distance from main faults, seismic intensity and lithology were selected as controlling factors for earthquake-triggered landslide susceptibility mapping. Qualitative susceptibility analyses were carried out using the map overlaying techniques in GIS platform. The validation result showed a success rate of 82.751 % between the susceptibility probability index map and the location of the initial landslide inventory. The predictive rate of 86.930 % was obtained by comparing the additional landslide polygons and the landslide susceptibility probability index map. Both the success rate and the predictive rate show sufficient agreement between the landslide susceptibility map and the existing landslide data, and good predictive power for spatial prediction of the earthquake-triggered landslides.  相似文献   

4.
Chong Xu  Xiwei Xu  Guihua Yu 《Landslides》2013,10(4):421-431
On 14 April 2010 at 07:49 (Beijing time), a catastrophic earthquake with Ms 7.1 struck Yushu County, Qinghai Province, China. A total of 2,036 landslides were interpreted from aerial photographs and satellite images, verified by selected field checking. These landslides cover about a total area of 1.194 km2. The characteristics and failure mechanisms of these landslides are presented in this paper. The spatial distribution of the landslides is evidently strongly controlled by the locations of the main co-seismic surface fault ruptures. The landslides commonly occurred close together. Most of the landslides are small; there were only 275 individual landslide (13.5 % of the total number) surface areas larger than 1,000 m2. The landslides are of various types. They are mainly shallow, disrupted landslides, but also include rock falls, deep-seated landslides, liquefaction-induced landslides, and compound landslides. Four types of factors are identified as contributing to failure along with the strong ground shaking: natural excavation of the toes of slopes, which mean erosion of the base of the slope, surface water infiltration into slopes, co-seismic fault slipping at landslide sites, and delayed occurrence of landslides due to snow melt or rainfall infiltration at sites where slopes were weakened by the co-seismic ground shaking. To analyze the spatial distribution of the landslides, the landslide area percentage (LAP) and landslide number density (LND) were compared with peak ground acceleration (PGA), distance from co-seismic main surface fault ruptures, elevation, slope gradient, slope aspect, and lithology. The results show landslide occurrence is strongly controlled by proximity to the main surface fault ruptures, with most landslides occurring within 2.5 km of such ruptures. There is no evident correlation between landslide occurrences and PGA. Both LAP and LND have strongly positive correlations with slope gradient, and additionally, sites at elevations between 3,800 and 4,000 m are relatively susceptible to landslide occurrence; as are slopes with northeast, east, and southeast slope aspects. Q4 al-pl, N, and T3 kn 1 have more concentrated landslide activity than others. This paper provides a detailed inventory map of landslides triggered by the 2010 Yushu earthquake for future seismic landslide hazard analysis and also provides a study case of characteristics, failure mechanisms, and spatial distribution of landslides triggered by slipping-fault generated earthquake on a plateau.  相似文献   

5.
遗传算法优化BP网络在滑坡灾害预测中的应用研究   总被引：1，自引：0，他引：1       下载免费PDF全文
在陕西省宝鸡市附近长寿沟地区滑坡详细调查和遥感解译的基础上,完成了1∶10000滑坡编目图。通过使用GIS的水文分析功能,运用正反DEM技术,将长寿沟地区划分为216个自然斜坡单元,其中包括123个滑坡单元和93个未发生滑坡单元,分析滑坡发生与坡高、坡度、坡向、坡形、人类工程活动和水文地质条件影响因子之间的统计规律。利用经遗传算法优化后的BP神经网络对80个滑坡样本和40个未滑坡样本进行训练学习,然后再利用训练好的网络对预测样本进行评价分析。结果表明:43个已滑坡单元中只有3个被误判为无滑坡,正确率为9302%,53个未滑坡单元中有10个被预测为滑坡,正确率为8113%,总体正确率为8646%。通过对被预测为滑坡的10个斜坡单元进行分析,发现这些单元在坡形、坡高等影响因素的组合上已经具备了发生滑坡的条件,虽然目前没有发生滑坡,但作为潜在的滑坡危险区,可以为滑坡灾害预测预报和防灾减灾工作提供参考。  相似文献   

6.
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.  相似文献   

7.
The 2015 Mw7.8 Gorkha earthquake triggered thousands of landslides of various types scattered over a large area. In the current study, we utilized pre- and post-earthquake high-resolution satellite imagery to compile two landslide inventories before and after earthquake and prepared three landslide susceptibility maps within 404 km2 area using frequency ratio (FR) model. From the study, we could map about 519 landslides including 178 pre-earthquake slides and 341 coseismic slides were identified. This study investigated the relationship between landslide occurrence and landslide causative factors, i.e., slope, aspect, altitude, plan curvature, lithology, land use, distance from streams, distance from road, distance from faults, and peak ground acceleration. The analysis showed that the majority of landslides both pre-earthquake and coseismic occurred at slope >30°, preferably in S, SE, and SW directions and within altitude ranging from 1000 to 1500 m and 1500 to 3500 m. Scatter plots between number of landslides per km?2 (LN) and percentage of landslide area (LA) and causative factors indicate that slope is the most influencing factor followed by lithology and PGA for the landslide formation. Higher landslide susceptibility before earthquake is observed along the road and rivers, whereas landslides after earthquake are triggered at steeper slopes and at higher altitudes. Combined susceptibility map indicates the effect of topography, geology, and land cover in the triggering of landslides in the entire basin. The resultant landslide susceptibility maps are verified through AUC showing success rates of 78, 81, and 77%, respectively. These susceptibility maps are helpful for engineers and planners for future development work in the landslide prone area.  相似文献   

8.
The objective of this study was to produce and evaluate a landslide susceptibility map for weathered granite soils in Deokjeok-ri Creek, South Korea. The relative effect (RE) method was used to determine the relationship between landslide causative factors (CFs) and landslide occurrence. To determine the effect of CFs on landslides, data layers of aspect, elevation, slope, internal relief, curvature, distance to drainage, drainage density, stream power index, sediment transport index, topographic wetness index, soil drainage character, soil type, soil depth, forest type, timber age, and geology were analyzed in a geographical information system (GIS) environment. A GIS-based landslide inventory map of 748 landslide locations was prepared using data from previous reports, aerial photographic interpretation, and extensive field work. A RE model was generated from a training set consisting of 673 randomly selected landslides in the inventory map, with the remaining 75 landslides used for validation of the susceptibility map. The results of the analysis were verified using the landslide location data. According to the analysis, the RE model had a success rate of 86.3 % and a predictive accuracy of 88.6 %. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations. The results of this study can therefore be used to mitigate landslide-induced hazards and to plan land use.  相似文献   

9.
This paper presents a methodology for developing a landslide hazard zonation map by integration of global positioning system (GPS), geographic information system (GIS), and remote sensing (RS) for Western Himalayan Kaghan Valley of Pakistan. The landslides in the study area have been located and mapped by using GPS. Eleven causative factors such as landuse, elevation, geology, rainfall intensity, slope inclination, soil, slope aspect, distances from main road, distances from secondary roads, and distances from main river and those from trunk streams were analyzed for occurrence of landslides. These factors were used with a modified form of pixel-based information value model to obtain landslide hazard zones. The matrix analysis was performed in remote sensing to produce a landslide hazard zonation map. The causative factors with the highest effect of landslide occurrence were landuse, rainfall intensity, distances from main road, distances from secondary roads, and distances from main river and those from trunk streams. In conclusion, we found that landslide occurrence was only in moderate, high, or very high hazard zones, and no landslides were in low or very low hazard zones showing 100% accuracy of our results. The landslide hazard zonation map showed that the current main road of the valley was in the zones of high or very high hazard. Two new safe road routes were suggested by using the GIS technology.  相似文献   

10.
The main goal of this paper is to generate a landslide susceptibility map through evidential belief function (EBF) model by using Geographic Information System (GIS) for Qianyang County, Shaanxi Province, China. At first, a detailed landslide inventory map was prepared, and the following ten landslide-conditioning factors were collected: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, distance to rivers, geomorphology, lithology, and rainfall. The landslides were detected from the interpretation of aerial photographs and supported by field surveys. A total of 81 landslides were randomly split into the following two parts: the training dataset 70 % (56 landslides) were used for establishing the model and the remaining 30 % (25 landslides) were used for the model validation. The ArcGIS was used to analyze landslide-conditioning factors and evaluate landslide susceptibility; as a result, a landslide susceptibility map was generated by using EBF and ArcGIS 10.0, thus divided into the following five susceptibility classes: very low, low, moderate, high, and very high. Finally, when we validated the accuracy of the landslide susceptibility map, both the success-rate and prediction-rate curve methods were applied. The results reveal that a final susceptibility map has the success rate of 83.31 % and the prediction rate of 79.41 %.  相似文献   

11.
Chong Xu  Xiwei Xu 《Natural Hazards》2014,72(2):871-893
The April 14, 2010 Yushu, China, earthquake (Mw 6.9) triggered a great number of landslides. At least 2,036 co-seismic landslides, with a total coverage area of 1.194 km2, were delineated by visual interpretation of aerial photographs and satellite images taken following the earthquake, and verified by field inspection. Based on the mapping results, a statistical analysis of the spatial distribution of these landslides is performed using the landslide area percentage (LAP), defined as the percentage of the area affected by the landslides, and landslide number density (LND), defined as the number of landslides per square kilometer. The purpose is to clarify how the landslides correlate the control factors, which are the elevation, slope angle, slope aspect, slope position, distance from drainages, lithology, distance from the surface rupture, and peak ground acceleration (PGA). The results show that both LAP and LND have strongly positive correlations with slope angle and negative correlations with distance from the surface rupture and distance from drainages. The highest LAP and LPD values are in places of elevations from 3,800 to 4,000 m. The slopes producing landslides are mostly facing toward NE, E, and SE. The geological units of Q4 al-pl, N, and T3 kn 1 have the highest concentrations of co-seismic landslides. No apparent correlations are present between LAP and LND values and PGA. On both sides of the surface rupture, the landslide distributions are almost similar except a few exceptions, likely associated with the nature of the strike-slip seismogenic fault for this event. The bivariate statistical analysis shows that, in descending order, the earthquake-triggered landslide impact factors are distance from surface rupture > slope angle > distance from drainages > lithology > PGA. Besides, as the detailed co-seismic landslides inventories related to strike-slip earthquakes are still few compared with that of thrusting-fault earthquakes, this case study would shed new light on the subject. For instance, the landslide spatial distribution on both sides of the strike-slip seismogenic fault is rather different from that of thrusting-fault earthquakes. It reminds us to take different strategies of measures for prevention and mitigation of landslides induced by earthquakes with different mechanisms.  相似文献   

12.
通过对攀枝花大河流域仁和街幅地质灾害的遥感解译和现场调查,共获得地质灾害点61处,其中以崩塌滑坡为主,泥石流次之。在此基础上,利用GIS空间分析方法对灾害点的空间分布与距水系距离、距断层距离、地形坡度、海拔高程、地层岩性的关系进行统计分析。结果表明:地质灾害点分布较少;崩塌滑坡主要受水系控制,沿水系呈线状分布,在距离水系300m范围内分布密度最大;海拔高程、地层岩性和距断层距离都是影响崩塌滑坡分布的重要因素;崩塌滑坡主要分布在海拔高度1 300~1 600m范围内;距断层1km范围内,崩塌滑坡分布密度最大;软弱半成砂岩、泥岩出露的地方更易发生地质灾害;在坡度0°~15°范围内,分布密度最大。  相似文献   

13.
This paper presents the results of geographical information system (GIS)-based landslide susceptibility mapping in Ayvalık, western Turkey using multi-criteria decision analysis. The methodology followed in the study includes data production, standardization, and analysis stages. A landslide inventory of the study area was compiled from aerial photographs, satellite image interpretations, and detailed field surveys. In total, 45 landslides were recorded and mapped. The areal extent of the landslides is 1.75 km2. The identified landslides are mostly shallow-seated, and generally exhibit progressive character. They are mainly classified as rotational, planar, and toppling failures. In all, 51, 45, and 4% of the landslides mapped are rotational, planar, and toppling types, respectively. Morphological, geological, and land-use data were produced using existing topographical and relevant thematic maps in a GIS framework. The considered landslide-conditioning parameters were slope gradient, slope aspect, lithology, weathering state of the rocks, stream power index, topographical wetness index, distance from drainage, lineament density, and land-cover and vegetation density. These landslide parameters were standardized in a common data scale by fuzzy membership functions. Then, the degree to which each parameter contributed to landslides was determined using the analytical hierarchy process method, and the weight values of these parameters were calculated. The weight values obtained were assigned to the corresponding parameters, and then the weighted parameters were combined to produce a landslide susceptibility map. The results obtained from the susceptibility map were evaluated with the landslide location data to assess the reliability of the map. Based on the findings obtained in this study, it was found that 5.19% of the total area was prone to landsliding due to the existence of highly and completely weathered lithologic units and due to the adverse effects of topography and improper land use.  相似文献   

14.
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15.
For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence, such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42% in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover.  相似文献   

16.
The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning.  相似文献   

17.
Panday  Suman  Dong  Jia-Jyun 《Landslides》2021,18(12):3875-3889

Continuous 5-day (August 4–9, 2019) torrential rainfall in the monsoon season triggered more than 90 landslides on northwest-southeast extended mountain range of Mon State, Myanmar. In this study, remote sensing images, DEM, and limited fieldworks were used to create the landslide inventory. The topography features of these landslides are analyzed via ArcGIS. The largest one occurred on 9 August 2019 and caused 75 deaths and 27 buildings were damaged. This landslide occurred on gentle topography (slope angle, 23°) with long run-out, in which the angle of reach was relatively low (10°). The volume was 111,878 m3 was mainly composed of weathered granite and red soil and the sliding depth was approximately 7.5 m. Topographic characteristics including the relative slope height, angle of reach, and slope angle of source area of 35 landslides with areas?>?4000 m2 were analyzed. The spatial distribution characteristics and topographic features of the 35 landslides below are distinguished: (1) the concentration of most of landslides on southwest-facing slopes showing the heterogeneous spatial distribution of landslide; (2) an uncommon landslide distribution in which more than half of landslide originates from upper slope; (3) the range of the angle of the source area (17°–38°) compatible with the internal friction angle of soils in tropical regions (17°–33°); and (4) the tangent of the angle of reach is generally smaller than 0.5 (angle of reach?<?27°) shows a relative high mobility and the relation between landslide mobility and the slope angle of the landslide source area is similar to the one of earthquake-triggered landslides, even though the triggering mechanism, landslide type, and landslide volume are dramatically different.

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18.
On May 12, 2008, at 1428 hours (Beijing time), a catastrophic earthquake, with a magnitude of Ms 8.0, struck the Sichuan Province, China. About 200,000 landslides, as a secondary geological hazard associated with the earthquake, were triggered over a broad area. These landslides were of almost all types such as shallow, disrupted landslides, rock falls, deep-seated landslides, and rock avalanches. Some of these landslides damaged and destroyed large part of some towns, blocked roads, dammed rivers, and caused other serious damages. The purpose of this study is to detect correlations between landslide occurrence and the surface rupture plane, ground shaking conditions (measured by peak ground acceleration, PGA), lithology, slope gradient, slope aspect, topographic position, and distance from drainages by using two indices, landslide area percentage (LAP) and the landslide number density (LND), based on geographic information system (GIS) technology and statistical analysis method in a square region (study area) of Beichuan County, Sichuan Province, China. There were 5,096 landslides related with the earthquake which were delineated by visual interpretation and selected field checking throughout the study area. The total area (horizontal projection) of the 5,096 landslides is about 41.103 km2. The LAP, which is defined as the percentage of the plane area affected by landslides, was 10.276 %, and the LND, means the number of landslides per square kilometers, was 12.74 landslides/km2. Statistical analysis results show that both LAP and LND have a positive correlation with slope gradient and a negative correlation with distance from the surface rupture. However, the correlation between the occurrence of landslides with PGA, topographic position, and distance from drainages are uncertain, or has just a little positive correlation. The correlation between landslide and slope aspect also shows the effect of the directivity of the seismic wave. The Zbq formation had the most concentrated landslide activity with the LND value of 21.78 landslides/km , 2 and the ∈1 q Gr. geological units had the highest LAP value. Furthermore, weight index (W i) model is performed with a GIS platform to derive landslide hazard index map. The success rate of the model was 71.615 % and, thus, it was valid. In addition, comparison of five landslide controlling parameters’ influence on landslide occurrences was also carried out.  相似文献   

19.
Landslide hazard zonation is essential for planning future developmental activities. At the present study, after the preparation of a landslide inventory of the study area, nine factors as well as sub-data layers of factor class weights were tested for an integrated analysis of landslide hazard in the region. The produced factor maps were weighted with the analytic hierarchy process method and then classified into four classes—negligible, low, moderate, and high. The final produced map for landslide hazard zonation in Golestan watershed revealed that: (1) about 53.85 % of the basin is prone to moderate and high threats of landslides. (2) Landslide events at the Golestan watershed were strongly correlated to the slope angle of the basin. It was observed that the active landslide zones, including moderate to high landslide hazard classes, have a high correlation to slope classes over 30° (R 2?=?0.769). (3) The regions most susceptible to landslide hazard are those located south and southwest of the watershed, which included rock topples, falls, and debris landslides.  相似文献   

20.
The objective of this study was to validate the outcomes of a modified decision tree classifier by comparing the produced landslide susceptibility map and the actual landslide occurrence, in an area of intensive landslide manifestation, in Xanthi Perfection, Greece. The values that concerned eight landslide conditioning factors for 163 landslides and 163 non-landslide locations were extracted by using advanced spatial GIS functions. Lithological units, elevation, slope angle, slope aspect, distance from tectonic features, distance from hydrographic network, distance from geological boundaries and distance from road network were among the eight landslide conditioning factors that were included in the landslide database used in the training phase. In the present study, landslide and non-landslide locations were randomly divided into two subsets: 80 % of the data (260 instances) were used for training and 20 % of the data (66 instances) for validating the developed classifier. The outcome of the decision tree classifier was a set of rules that expressed the relationship between landslide conditioning factors and the actual landslide occurrence. The landslide susceptibility belief values were obtained by applying a statistical method, the certainty factor method, and by measuring the belief in each rule that the decision tree classifier produced, transforming the discrete type of result into a continuous value that enabled the generation of a landslide susceptibility belief map. In total, four landslide susceptibility maps were produced using the certainty factor method, the Iterative Dichotomizer version 3 algorithm, the J48 algorithm and the modified Iterative Dichotomizer version 3 model in order to evaluate the performance of the developed classifier. The validation results showed that area under the ROC curves for the models varied from 0.7936 to 0.8397 for success rate curve and 0.7766 to 0.8035 for prediction rate curves, respectively. The success rate and prediction curves showed that the modified Iterative Dichotomizer version 3 model had a slightly higher performance with 0.8397 and 0.8035, respectively. From the outcomes of the study, it was induced that the developed modified decision tree classifier could be efficiently used for landslide susceptibility analysis and in general might be used for classification and estimation purposes in spatial predictive models.  相似文献   

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