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Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models 总被引：23，自引：5，他引：18
This paper summarizes findings of landslide hazard analysis on Penang Island, Malaysia, using frequency ratio, logistic regression, and artificial neural network models with the aid of GIS tools and remote sensing data. Landslide locations were identified and an inventory map was constructed by trained geomorphologists using photo-interpretation from archived aerial photographs supported by field surveys. A SPOT 5 satellite pan sharpened image acquired in January 2005 was used for land-cover classification supported by a topographic map. The above digitally processed images were subsequently combined in a GIS with ancillary data, for example topographical (slope, aspect, curvature, drainage), geological (litho types and lineaments), soil types, and normalized difference vegetation index (NDVI) data, and used to construct a spatial database using GIS and image processing. Three landslide hazard maps were constructed on the basis of landslide inventories and thematic layers, using frequency ratio, logistic regression, and artificial neural network models. Further, each thematic layer’s weight was determined by the back-propagation training method and landslide hazard indices were calculated using the trained back-propagation weights. The results of the analysis were verified and compared using the landslide location data and the accuracy observed was 86.41, 89.59, and 83.55% for frequency ratio, logistic regression, and artificial neural network models, respectively. On the basis of the higher percentages of landslide bodies predicted in very highly hazardous and highly hazardous zones, the results obtained by use of the logistic regression model were slightly more accurate than those from the other models used for landslide hazard analysis. The results from the neural network model suggest the effect of topographic slope is the highest and most important factor with weightage value (1.0), which is more than twice that of the other factors, followed by the NDVI (0.52), and then precipitation (0.42). Further, the results revealed that distance from lineament has the lowest weightage, with a value of 0. This shows that in the study area, fault lines and structural features do not contribute much to landslide triggering. 相似文献
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Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements: the case study of Paphos area in Cyprus 总被引：1，自引：0，他引：1
D. D. Alexakis A. Agapiou M. Tzouvaras K. Themistocleous K. Neocleous S. Michaelides D. G. Hadjimitsis 《Natural Hazards》2014,72(1):119-141
This study considers the impact of landslides on transportation pavements in rural road network of Cyprus using remote sensing and geographical information system (GIS) techniques. Landslides are considered to be one of the most extreme natural hazards worldwide, causing both human losses and severe damages to the transportation network. Risk assessment for monitoring a road network is based on the combination of the probability of landslides occurrence and the extent and severity of the resultant consequences should the disasters (landslides) occur. Factors that can trigger landslide episodes include proximity to active faults, geological formations, fracture zones, degree and high curvature of slopes, water conditions, etc. In this study, the reliability and vulnerability of a rural network are examined. Initially, landslide locations were identified from the interpretation of satellite images. Different geomorphological factors such as aspect, slope, distance from the watershed, lithology, distance from lineaments, topographic curvature, land use and vegetation regime derived from satellite images were selected and incorporated in GIS environment in order to develop a decision support and continuous landslide monitoring system of the area. These parameters were then used in the final landslide hazard assessment model based on the analytic hierarchy process method. The results indicated good correlation between classified high-hazard areas and field-confirmed slope failures. The CA Markov model was also used to predict the landslide hazard zonation map for 2020 and the possible future hazards for transportation pavements. The proposed methodology can be used for areas with similar physiographic conditions all over the Eastern Mediterranean region. 相似文献
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利用光学遥感数据、GIS及人工神经网络模型分析区域滑坡灾害(英文) 总被引：3，自引：0，他引：3
Biswajeet Pradhan Saro Lee 《地学前缘》2007,14(6):143-152
用光学遥感数据和地理信息系统(GIS)分析了马来西亚Selangor地区的滑坡灾害。通过遥感图像解译和野外调查,在研究区内确定出滑坡发生区。通过GIS和图像处理,建立了一个集地形、地质和遥感图像等多种信息的空间数据库。滑坡发生的因素主要为:地形坡度、地形方位、地形曲率及与排水设备距离;岩性及与线性构造距离;TM图像解译得到的植被覆盖情况;Landsat图像解译得到的植被指数;降水量。通过建立人工神经网络模型对这些因素进行分析后得到滑坡灾害图:由反向传播训练方法确定每个因素的权重值,然后用该权重值计算出滑坡灾害指数,最后用GIS工具生成滑坡灾害图。用遥感解译和野外观测确定出的滑坡位置资料验证了滑坡灾害图,准确率为82.92%。结果表明推测的滑坡灾害图与滑坡实际发生区域足够吻合。 相似文献
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Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models 总被引：15，自引：10，他引：5
This paper presents landslide hazard analysis at Cameron area, Malaysia, using a geographic information system (GIS) and remote sensing data. Landslide locations were identified from interpretation of aerial photographs and field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence are topographic slope, topographic aspect, topographic curvature, and distance to rivers, all from the topographic database; lithology and distance to faults were taken from the geologic database; land cover from TM satellite image; the vegetation index value was taken from Landsat images; and precipitation distribution from meteorological data. Landslide hazard area was analyzed and mapped using the landslide occurrence factors by frequency ratio and bivariate logistic regression models. The results of the analysis were verified using the landslide location data and compared with the probabilistic models. The validation results showed that the frequency ratio model (accuracy is 89.25%) is better in prediction of landslide than bivariate logistic regression (accuracy is 85.73%) model. 相似文献
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Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley,Italy 总被引：6，自引：3，他引：3
F. Falaschi F. Giacomelli P. R. Federici A. Puccinelli G. D’Amato Avanzi A. Pochini A. Ribolini 《Natural Hazards》2009,50(3):551-569
This article presents a multidisciplinary approach to landslide susceptibility mapping by means of logistic regression, artificial
neural network, and geographic information system (GIS) techniques. The methodology applied in ranking slope instability developed
through statistical models (conditional analysis and logistic regression), and neural network application, in order to better
understand the relationship between the geological/geomorphological landforms and processes and landslide occurrence, and
to increase the performance of landslide susceptibility models. The proposed experimental study concerns with a wide research
project, promoted by the Tuscany Region Administration and APAT-Italian Geological Survey, aimed at defining the landslide
hazard in the area of the Sheet 250 “Castelnuovo di Garfagnana” (1:50,000 scale). The study area is located in the middle
part of the Serchio River basin and is characterized by high landslide susceptibility due to its geological, geomorphological,
and climatic features, among the most severe in Italy. Terrain susceptibility to slope failure has been approached by means
of indirect-quantitative statistical methods and neural network software application. Experimental results from different
methods and the potentials and pitfalls of this methodological approach have been presented and discussed. Applying multivariate
statistical analyses made it possible a better understanding of the phenomena and quantification of the relationship between
the instability factors and landslide occurrence. In particular, the application of a multilayer neural network, equipped
for supervised learning and error control, has improved the performance of the model. Finally, a first attempt to evaluate
the classification efficiency of the multivariate models has been performed by means of the receiver operating characteristic
(ROC) curves analysis approach. 相似文献
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This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS)
and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs
and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing
tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are
topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance
from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation
amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility
was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis
were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%.
The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis
in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard
map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing
infrastructure like transportation network. 相似文献
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Pathways for adaptive and integrated disaster resilience 总被引：7，自引：2，他引：5
Djalante Riyanti Holley Cameron Thomalla Frank Carnegie Michelle 《Natural Hazards》2013,65(3):2105-2128
The GIS-multicriteria decision analysis (GIS-MCDA) technique is increasingly used for landslide hazard mapping and zonation. It enables the integration of different data layers with different levels of uncertainty. In this study, three different GIS-MCDA methods were applied to landslide susceptibility mapping for the Urmia lake basin in northwest Iran. Nine landslide causal factors were used, whereby parameters were extracted from an associated spatial database. These factors were evaluated, and then, the respective factor weight and class weight were assigned to each of the associated factors. The landslide susceptibility maps were produced based on weighted overly techniques including analytic hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA). An existing inventory of known landslides within the case study area was compared with the resulting susceptibility maps. Respectively, Dempster-Shafer Theory was used to carry out uncertainty analysis of GIS-MCDA results. Result of research indicated the AHP performed best in the landslide susceptibility mapping closely followed by the OWA method while the WLC method delivered significantly poorer results. The resulting figures are generally very high for this area, but it could be proved that the choice of method significantly influences the results. 相似文献
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地质灾害危险性评价是防灾减灾工作的重要依据。本文以汶川县城周边64 km2为例,应用遥感信息提取技术与GIS空间分析方法,根据IKONOS遥感图像和地形图及野外调查资料,提取了崩塌和滑坡易发性评价因子,采用信息量法确定了因子分值,计算了崩塌和滑坡易发性,并分别提出崩塌和滑坡的危险性计算方法,形成了汶川地区崩塌和滑坡危险性分区图。研究结果表明:新的崩塌和滑坡危险性评价方法能够反映区内地质灾害危险程度,该方法可行,结果合理,这为中、大比例尺区域范围内地质灾害危险性研究提供了有益的思路。 相似文献
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基于RS与GIS的层次分析法在滑坡危险性评估中的应用——以四川宣汉天台乡滑坡为例 总被引：5，自引：1，他引：4
基于遥感(RS)和地理信息系统(GIS)技术,采用多层次分析(AHP)法,以四川宣汉天台乡为研究区,根据该区实际情况,选取线性构造、道路、土地利用、坡度、坡向5种影响滑坡灾害发生的因素作为评价因子,进行区域滑坡危险性评估。在ArcGIS的空间分析环境中运行权重叠加,把研究区划分成滑坡极易发生区、易发生区、一般发生区、可能发生区、难发生区和极难发生区。通过实地调查和与研究区的滑坡灾害实证研究结果进行比较,发现评估结果与实际状况较为吻合,研究方法能够准确地评估区域滑坡灾害危险性的程度。 相似文献
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For the socio-economic development of a country, the highway network plays a pivotal role. It has therefore become an imperative to have landslide hazard assessment along these roads to provide safety. The current study presents landslide hazard zonation maps, based on the information value method and frequency ratio method using GIS on 1:50,000 scale by generating the information about the landslide influencing factors. The study was carried out in the year 2017 on a part of Ravi river catchment along one of the landslide-prone Chamba to Bharmour road corridor of NH-154A in Himachal Pradesh, India. A number of landslide triggering geo-environmental factors like “slope, aspect, relative relief, soil, curvature, Land Use and Land Cover (LULC), lithology, drainage density, and lineament density” were selected for landslide hazard mapping based on landslide inventory. The landslide inventory has been developed using satellite imagery, Google earth and by doing exhaustive field surveys. A digital elevation model was used to generate slope gradient, slope aspect, curvature, and relative relief map of the study area. The other information, i.e., soil maps, geological maps, and toposheets, have been collected from various departments. The landslide hazard zonation map was categorized namely “very high hazard, high hazard, medium hazard, low hazard, and very low hazard.” The results from these two methods have been validated using area under curve (AUC) method. It has been found that hazard zonation map prepared using frequency ratio model had a prediction rate of 75.37% while map prepared using information value method had prediction rate of 78.87%. Hence, on the basis of prediction rate, the landslide hazard zonation map, obtained using information value method, was experienced to be more suitable for the study area. 相似文献
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Subrata Mondal Sujit Mandal 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2018,12(1):29-44
The present study deals with the preparation of a landslide susceptibility map of the Balason River basin, Darjeeling Himalaya, using a logistic regression model based on Geographic Information System and Remote Sensing. The landslide inventory map was prepared with a total of 295 landslide locations extracted from various satellite images and intensive field survey. Topographical maps, satellite images, geological, geomorphological, soil, rainfall and seismic data were collected, processed and constructed into a spatial database in a GIS environment. The chosen landslide-conditioning factors were altitude, slope aspect, slope angle, slope curvature, geology, geomorphology, soil, land use/land cover, normalised differential vegetation index, drainage density, lineament number density, distance from lineament, distance to drainage, stream power index, topographic wetted index, rainfall and peak ground acceleration. The produced landslide susceptibility map satisfied the decision rules and ?2 Log likelihood, Cox &; Snell R-Square and Nagelkerke R-Square values proved that all the independent variables were statistically significant. The receiver operating characteristic curve showed that the prediction accuracy of the landslide probability map was 96.10%. The proposed LR method can be used in other hazard/disaster studies and decision-making. 相似文献
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白龙江流域是我国四大地质灾害高发区之一,也是全国17个地质灾害重点防治区之一。第四纪沉积物的广泛分布为地质灾害提供了丰富的物质基础。本文基于遥感和GIS技术,结合影像特征和野外实际调查,建立了研究区第四纪成因类型（5大类11亚类）的解译标志;运用所建立的解译标志,对研究区进行了精细解译,新增第四纪残积物、坡积物、泥石流堆积和人工堆积等8个沉积亚类,补充和完善了滑坡堆积体和崩积物的面域数据,共解译第四纪沉积物面积444.7 km2,较前人研究资料扩展了380.4 km2;研究区上游段主要受地形地貌因素影响,第四纪沉积物沿白龙江主河道及其支流分布,中游段主要受地质构造控制,第四纪沉积物沿活动断层呈条带状分布,下游段主要受地层岩性影响,第四纪沉积物呈片状分布;第四纪沉积物灾害效应主要表现为崩滑效应和对泥石流的补给效应。本文研究成果对研究区区域地质灾害调查和风险评价提供了技术支撑。 相似文献
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Kaixiang Zhang Xueling Wu Ruiqing Niu Ke Yang Lingran Zhao 《Environmental Earth Sciences》2017,76(11):405
Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management. 相似文献
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Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland,Malaysia 总被引：20，自引：6，他引：14
This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information
system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial
photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed
into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope,
topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall,
land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed
using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined
by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation
weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model
suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the
other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to
validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation
results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas. 相似文献
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针对光滑粒子流体动力学方法（SPH）在滑坡模拟中建立粒子模型的难题,提出了基于地理信息系统（GIS）栅格数据的粒子排列与插入方法。根据该方法,建立了滑坡SPH粒子模型及相关粒子生成程序,进一步以结合摩尔-库仑破坏准则的SPH宾汉流体模型为核心,实现了运用SPH方法模拟滑坡破坏后三维运动的过程。该SPH模型在对唐家山滑坡的模拟中得到了验证,并预测了金坪子滑坡破坏后的影响范围。结果表明：基于GIS空间数据的滑坡SPH粒子模型具有可行性与良好的适用性。以GIS数据库为基础,开展滑坡灾害的模拟研究,将大大提高对滑坡等地质灾害的仿真分析,为滑坡灾害的预测与防治提供参考。 相似文献
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Giovanni Gullà Dario Peduto Luigi Borrelli Loredana Antronico Gianfranco Fornaro 《Landslides》2017,14(1):171-188
The geometric and kinematic characterization of landslides affecting urban areas is a challenging goal that is routinely pursued via geological/geomorphological method and monitoring of ground displacements achieved by geotechnical and, more recently, advanced differential interferometric synthetic aperture radar (A-DInSAR) data. Although the integration of all the above-mentioned methods should be planned a priori to be more effective, datasets resulting from the independent use of these different methods are commonly available, thus making crucial the need for their standardized a posteriori integration. In this regard, the present paper aims to provide a contribution by introducing a procedure that, taking into account the specific limits of geological/geomorphological analyses and deep/surface ground displacement monitoring via geotechnical and A-DInSAR data, allows the a posteriori integration of the results by exploiting their complementarity for landslide characterization. The approach was tested in the urban area of Lungro village (Calabria region, southern Italy), which is characterized by complex geological/geomorphological settings, widespread landslides and peculiar urban fabric. In spite of the different level of information preliminarily available for each landslide as result of the independent use of the three methods, the implementation of the proposed procedure allowed a better understanding and typifying of the geometry and kinematics of 50 landslides. This provided part of the essential background for geotechnical landslide models to be used for slope stability analysis within landslide risk mitigation strategies. 相似文献
20.
The purpose of this study is to assess the susceptibility of landslides around Yomra and Arsin towns near Trabzon, in northeast
of Turkey, using a geographical information system (GIS). Landslide inventory of the area was made by detailed field surveys
and the analyses of the topographical map. The landslide triggering factors are considered to be slope angle, slope aspect,
distance from drainage, distance from roads and the weathered lithological units, which were called as “geotechnical units”
in the study. Idrisi and ArcGIS packages manipulated all the collected data. Logistic regression (LR) and weighted linear
combination (WLC) statistical methods were used to create a landslide susceptibility map for the study area. The results were
assessed within the scope of two different points: (a) effectiveness of the methods used and (b) effectiveness of the environmental
casual parameters influencing the landslides. The results showed that the WLC model is more suitable than the LR model. Regarding
the casual parameters, geotechnical units and slopes were found to be the most important variables for estimating the landslide
susceptibility in the study area. 相似文献