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1.
Application of frequency ratio,weights of evidence and evidential belief function models in landslide susceptibility mapping 总被引：2，自引：1，他引：1
The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. 相似文献
2.
Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model 总被引：10，自引：0，他引：10
The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks
with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in
the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as
well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing.
Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature;
2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage;
and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural
network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation
training method has been used for the selection of the five different random training sites in order to calculate the factor’s
weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard
maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide
test locations that were not used during the training phase of the neural network. Our findings of verification results show
an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently
analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis.
The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide
areas. 相似文献
3.
S. Kundu A. K. Saha D. C. Sharma C. C. Pant 《Journal of the Indian Society of Remote Sensing》2013,41(3):697-709
A comprehensive Landslide Susceptibility Zonation (LSZ) map is sought for adopting any landslide preventive and mitigation measures. In the present study, LSZ map of landslide prone Ganeshganga watershed (known for Patalganga Landslide) has been generated using a binary logistic regression (BLR) model. Relevant thematic layers pertaining to the causative factors for landslide occurrences, such as slope, aspect, relative relief, lithology, tectonic structures, lineaments, land use and land cover, distance to drainage, drainage density and anthropogenic factors like distance to road, have been generated using remote sensing images, field survey, ancillary data and GIS techniques. The coefficients of the causative factors retained by the BLR model along with the constant have been used to construct the landslide susceptibility map of the study area, which has further been categorized into four landslide susceptibility zones from high to very low. The resultant landslide susceptibility map was validated using receiver operator characteristic (ROC) curve analysis showing an accuracy of 95.2 % for an independent set of test samples. The result also showed a strong agreement between distribution of existing landslides and predicted landslide susceptibility zones. 相似文献
4.
The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas. 相似文献
5.
Mazlan Hashim Syarifuddin Misbari Amin Beiranvand Pour 《Journal of the Indian Society of Remote Sensing》2018,46(2):233-248
Integration of satellite remote sensing data and GIS techniques is an applicable approach for landslide mapping and assessment in highly vegetated regions with a tropical climate. In recent years, there have been many severe flooding and landslide events with significant damage to livestock, agricultural crop, homes, and businesses in the Kelantan river basin, Peninsular Malaysia. In this investigation, Landsat-8 and phased array type L-band synthetic aperture radar-2 (PALSAR-2) datasets and analytical hierarchy process (AHP) approach were used to map landslide in Kelantan river basin, Peninsular Malaysia. Landslides were determined by tracking changes in vegetation pixel data using Landsat-8 images that acquired before and after flooding. The PALSAR-2 data were used for comprehensive analysis of major geological structures and detailed characterizations of lineaments in the state of Kelantan. AHP approach was used for landslide susceptibility mapping. Several factors such as slope, aspect, soil, lithology, normalized difference vegetation index, land cover, distance to drainage, precipitation, distance to fault, and distance to the road were extracted from remotely sensed data and fieldwork to apply AHP approach. The excessive rainfall during the flood episode is a paramount factor for numerous landslide occurrences at various magnitudes, therefore, rainfall analysis was carried out based on daily precipitation before and during flood episode in the Kelantan state. The main triggering factors for landslides are mainly due to the extreme precipitation rate during the flooding period, apart from the favorable environmental factors such as removal of vegetation within slope areas, and also landscape development near slopes. Two main outputs of this study were landslide inventory occurrences map during 2014 flooding episode and landslide susceptibility map for entire Kelantan state. Modeled/predicted landslides with a susceptible map generated prior and post-flood episode, confirmed that intense rainfall throughout Kelantan has contributed to produce numerous landslides with various sizes. It is concluded that precipitation is the most influential factor for landslide event. According to the landslide susceptibility map, 65% of the river basin of Kelantan is found to be under the category of low landslide susceptibility zone, while 35% class in a high-altitude segment of the south and south-western part of the Kelantan state located within high susceptibility zone. Further actions and caution need to be remarked by the local related authority of the Kelantan state in very high susceptibility zone to avoid further wealth and people loss in the future. Geo-hazard mitigation programs must be conducted in the landslide recurrence regions for reducing natural catastrophes leading to loss of financial investments and death in the Kelantan river basin. This investigation indicates that integration of Landsat-8 and PALSAR-2 remotely sensed data and GIS techniques is an applicable tool for Landslide mapping and assessment in tropical environments. 相似文献
6.
Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio,certainty factor,and index of entropy 总被引：3，自引：0，他引：3
The main objective of the study was to evaluate and compare the overall performance of three methods, frequency ratio (FR), certainty factor (CF) and index of entropy (IOE), for rainfall-induced landslide susceptibility mapping at the Chongren area (China) using geographic information system and remote sensing. First, a landslide inventory map for the study area was constructed from field surveys and interpretations of aerial photographs. Second, 15 landslide-related factors such as elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to faults, distance to rivers, distance to roads, landuse, NDVI, lithology and rainfall were prepared for the landslide susceptibility modelling. Using these data, three landslide susceptibility models were constructed using FR, CF and IOE. Finally, these models were validated and compared using known landslide locations and the receiver operating characteristics curve. The result shows that all the models perform well on both the training and validation data. The area under the curve showed that the goodness-of-fit with the training data is 79.12, 80.34 and 80.42% for FR, CF and IOE whereas the prediction power is 80.14, 81.58 and 81.73%, for FR, CF and IOE, respectively. The result of this study may be useful for local government management and land use planning. 相似文献
7.
GIS支持下滑坡灾害空间预测方法研究 总被引：11，自引：0，他引：11
滑坡预测在防灾减灾工作中具有重要意义,它包括空间、时间预测两个方面。基于统计模型进行区域评价与空间预测是滑坡灾害研究的重要方向,但是预测结果往往依赖样本数量和空间分布等。本文以马来西亚金马伦高原为研究区,选择高程、坡度、坡向、地表曲率、构造、土地覆盖、地貌类型、道路和排水系统作为评价因子,探讨运用地理信息系统(GIS)和遥感(RS)获取与管理滑坡灾害信息,以及热带雨林地区湿热环境下滑坡空间预测的方法。支持向量机(SVM)和逻辑(Logistic)回归模型分别应用于滑坡空间预测,结果显示平均预测精度分别为95.9%和86.2%,SVM法具有较高的描述精度,值得推荐;同时,基于SVM模型的滑坡空间预测受样本影响较小,预测结果相对比较稳定,这对于滑坡灾害区域评价与预测的快速实现具有实际意义。 相似文献
8.
A GIS-based statistical methodology for landslide susceptibility zonation is described and its application to a study area
in the Western Ghats of Kerala (India) is presented. The study area was approximately 218.44 km2 and 129 landslides were identified in this area. The environmental attributes used for the landslide susceptibility analysis
include geomorphology, slope, aspect, slope length, plan curvature, profile curvature, elevation, drainage density, distance
from drainages, lineament density, distance from lineaments and land use. The quantitative relationship between landslides
and factors affecting landslides are established by the data driven-Information Value (InfoVal) — method. By applying and
integrating the InfoVal weights using ArcGIS software, a continuous scale of numerical indices (susceptibility index) is obtained
with which the study area is divided into five classes of landslide susceptibility. In order to validate the results of the
susceptibility analysis, a success rate curve was prepared. The map obtained shows that a great majority of the landslides
(74.42%) identified in the field were located in susceptible and highly susceptible zones (27.29%). The area ratio calculated
by the area under curve (AUC) method shows a prediction accuracy of 80.45%. The area having a high scale of susceptibility
lies on side slope plateaus and denudational hills with high slopes where drainage density is relatively low and terrain modification
is relatively intense. 相似文献
9.
Landslide Susceptibility Analysis of Shiv-Khola Watershed, Darjiling: A Remote Sensing & GIS Based Analytical Hierarchy Process (AHP) 总被引：1，自引：0，他引：1
In the present study, Remote Sensing Technique and GIS tools were used to prepare landslide susceptibility map of Shiv-khola watershed, one of the landslide prone part of Darjiling Himalaya, based on 9 landslide inducing parameters like lithology, slope gradient, slope aspect, slope curvature, drainage density, upslope contributing area, land use and land cover, road contributing area and settlement density applying Analytical Hierarchy Approach (AHA). In this approach, quantification of the factors was executed on priority basis by pair-wise comparison of the factors. Couple comparing matrix of the factors were being made with reasonable consistency for understanding relative dominance of the factors as well as for assigning weighted mean/prioritized factor rating value for each landslide triggering factors through arithmetic mean method using MATLAB Software. The factor maps/thematic data layers were generated with the help of SOI Topo-sheet, LIIS-III Satellite Image (IRS P6/Sensor-LISS-III, Path-107, Row-052, date-18/03/2010) by using Erdas Imagine 8.5, PCI Geomatica, Arc View and ARC GIS Software. Landslide frequency (%) for each class of all the thematic data layers was calculated to assign the class weight value/rank value. Then, weighted linear combination (WLC) model was implied to determine the landslide susceptibility coefficient value (LSCV or ??M??) integrating factors weight and assigned class weight on GIS platform. Greater the value of M, higher is the propensity of landslide susceptibility over the space. Then Shivkhola watershed was classified into seven landslide susceptibility zones and the result was verified by ground truth assessment of existing landslide location where the classification accuracy was 92.86 and overall Kappa statistics was 0.8919. 相似文献
10.
11.
The main aim of this study was to produce landslide susceptibility maps using statistical index (SI), certainty factors (CF), weights of evidence (WoE) and evidential belief function (EBF) models for the Long County, China. Firstly, a landslide inventory map, including a total of 171 landslides, was compiled on the basis of earlier reports, interpretation of aerial photographs and supported by extensive field surveys. Thereafter, all landslides were randomly separated into two data sets: 70% landslides (120 points) were selected for establishing the model and the remaining landslides (51 points) were used for validation purposes. Eleven landslide conditioning factors, such as slope aspect, slope angle, plan curvature, profile curvature, altitude, distance to faults, distance to roads, distance to rivers, lithology, NDVI and land use, were considered for landslide susceptibility mapping in this study. Then, the SI, CF, WoE and EBF models were used to produce the landslide susceptibility maps for the study area. Finally, the four models were validated using area under the curve (AUC) method. According to the validation results, the EBF model (AUC = 78.93%) has a higher prediction accuracy than the SI model (AUC = 77.72%), the WoE model (AUC = 77.62%) and the CF model (AUC = 77.72%). Similarly, the validation results also indicate that the EBF model has the highest training accuracy of 80.25%, followed by SI (79.80%), WoE (79.71%) and CF (79.67%) models. 相似文献
12.
Binh Thai Pham Indra Prakash Abolfazl Jaafari Dieu Tien Bui 《Journal of the Indian Society of Remote Sensing》2018,46(9):1457-1470
In this study, the spatial prediction of rainfall-induced landslides at the Pauri Gahwal area, Uttarakhand, India has been done using Aggregating One-Dependence Estimators (AODE) classifier which has not been applied earlier for landslide problems. Historical landslide locations have been collated with a set of influencing factors for landslide spatial analysis. The performance of the AODE model has been assessed using statistical analyzing methods and receiver operating characteristic curve technique. The predictive capability of the AODE model has also been compared with other popular landslide models namely Support Vector Machines (SVM), Radial Basis Function Neural Network (ANN-RBF), Logistic Regression (LR), and Naïve Bayes (NB). The result of analysis illustrates that the AODE model has highest predictability, followed by the SVM model, the ANN-RBF model, the LR model, and the NB model, respectively. Thus AODE is a promising method for the development of better landslide susceptibility map for proper landslide hazard management. 相似文献
13.
M. Kannan E. Saranathan R. Anbalagan 《Journal of the Indian Society of Remote Sensing》2011,39(4):485-496
Landslide is a common natural hazard that usually occurs in mountainous areas. Rapid urban development and high traffic intensity
movements have been hampered to a great extent by phenomenon of landslides. In Ghat section, vertical cuttings and steep slopes
are induced slope failures. An assessment of landslide hazards is therefore a prerequisite for sustainable development of
the hilly region. In the present study, Macro Landslide Hazard Zonation was carried out in the Bodi – Bodimettu ghats section,
Western Ghats, Theni district. The slope spreads over an area of about 10.09 sq km encompassing Puliuttu Ar. sub-watershed.
The study was made with help of different types of data including Survey of India topographic map, geology map, important
inherent factors like lithology, structure, slope morphometry, relative relief, land use/land cover and hydrogeological conditions
using Bureau of Indian Standard (BSI 14496 (Part 2):1998) and related thematic maps. Based on the thematic layers, landslide
hazard evaluation factor (LHEF) and total estimated hazard (TEHD) were calculated and the macro hazard zonation map was prepared.
Landslide Hazard Zonation (LHZ) of the terrain shows that out of 17 facets, facets 1 to 5 and 8 falls under Moderate Hazard
zone category and facets 6, 7 and 9 to 17 under the High Hazard zone category. The field study with further analysis for hazard
concluded that about 68% of the total area falls in the high hazard zone. 相似文献
14.
AbstractIn this study, we introduced novel hybrid of evidence believe function (EBF) with logistic regression (EBF-LR) and logistic model tree (EBF-LMT) for landslide susceptibility modelling. Fourteen conditioning factors were selected, including slope aspect, elevation, slope angle, profile curvature, plan curvature, topographic wetness index (TWI), stream sediment transport index (STI), stream power index (SPI), distance to rivers, distance to faults, distance to roads, lithology, normalized difference vegetation index (NDVI), and land use. The importance of factors was assessed using correlation attribute evaluation method. Finally, the performance of three models was evaluated using the area under the curve (AUC). The validation process indicated that the EBF-LMT model acquired the highest AUC for the training (84.7%) and validation (76.5%) datasets, followed by EBF-LR and EBF models. Our result also confirmed that combination of a decision tree-logistic regression-based algorithm with a bivariate statistical model lead to enhance the prediction power of individual landslide models. 相似文献
15.
Landslide susceptibility mapping of a catchment area using frequency ratio,fuzzy logic and multivariate logistic regression approaches 总被引：30，自引：0，他引：30
B. Pradhan 《Journal of the Indian Society of Remote Sensing》2010,38(2):301-320
Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the
reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors.
This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility
mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide
locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images,
inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed
into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility
mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence
were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database;
lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation
index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the
fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility
mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results
of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate
logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction
than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators,
in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst
accuracy (69%). 相似文献
16.
Landslide hazards are a major natural disaster that affects most of the hilly regions around the world. In India, significant damages due to earthquake induced landslides have been reported in the Himalayan region and also in the Western Ghat region. Thus there is a requirement of a quantitative macro-level landslide hazard assessment within the Indian subcontinent in order to identify the regions with high hazard. In the present study, the seismic landslide hazard for the entire state of Karnataka, India was assessed using topographic slope map, derived from the Digital Elevation Model (DEM) data. The available ASTER DEM data, resampled to 50 m resolution, was used for deriving the slope map of the entire state. Considering linear source model, deterministic seismic hazard analysis was carried out to estimate peak horizontal acceleration (PHA) at bedrock, for each of the grid points having terrain angle 10° and above. The surface level PHA was estimated using nonlinear site amplification technique, considering B-type NEHRP site class. Based on the surface level PHA and slope angle, the seismic landslide hazard for each grid point was estimated in terms of the static factor of safety required to resist landslide, using Newmark’s analysis. The analysis was carried out at the district level and the landslide hazard map for all the districts in the Karnataka state was developed first. These were then merged together to obtain a quantitative seismic landslide hazard map of the entire state of Karnataka. Spatial variations in the landslide hazard for all districts as well as for the entire state Karnataka is presented in this paper. The present study shows that the Western Ghat region of the Karnataka state is found to have high landslide hazard where the static factor of safety required to resist landslide is very high. 相似文献
17.
18.
在降雨等外界诱发因素的综合作用下,滑坡位移预测是一个复杂的动力系统问题。利用三峡库区白家包滑坡综合监测数据,分析滑坡演化实时特征,提取影响滑坡变形的最相关因素,研究发现白家包滑坡为降雨主导型堆积层滑坡;采用自回归综合移动模型（ARIMA）模型进行拟合及预测,引入月累积降雨量对模型季节性趋势参数进行评估优化,对白家包滑坡72期月相对位移数据进行拟合及预测研究,最终模型结果和实测值的平均绝对误差和相关系数分别为2.873和0.983。研究结果表明,与传统经验法相比,优化参数模型更符合滑坡变形的一般规律。 相似文献
19.
滑坡体除了因自身重力产生位移外,还受到降雨的影响,但通常降雨对滑坡位移的作用具有滞后性。为了分析并预测降雨对滑坡位移的影响,本文提出一种顾及降雨影响的动态优化时滞时序GM(1,2)滑坡位移预测模型。首先,利用经验模态分解(EMD)分解位移序列并通过时间序列重构得到周期位移序列和趋势位移序列,对降雨数据和滑坡周期位移序列进行时滞分析和相关分析,确定时滞时间和影响程度,建立基于背景值优化的动态时滞GM(1,2)模型预测降雨量变化导致的滑坡周期位移变化;然后,建立门限自回归模型预测滑坡趋于自然变化的趋势位移;最后,通过时序叠加得到顾及降雨影响的滑坡预测位移,建立了顾及降雨因素的动态优化时滞时序GM(1,2)组合预测方法。本文以福宁高速公路八尺门滑坡和秭归县八字门滑坡监测数据为例,验证了动态优化时滞GM(1,2)模型的精度,并与其他模型的预测结果进行了对比分析。试验结果表明,动态优化时滞时序GM(1,2)组合预测模型能准确地预测降雨影响导致的滑坡位移变化,预测效果较好,该组合模型对滑坡灾害的预警与防治具有一定的参考价值。 相似文献
20.
Piyoosh Rautela Ramesh Chandra Lakhera 《International Journal of Applied Earth Observation and Geoinformation》2000,2(3-4)
The area around Sataun in the Sirmur district of Himachal Pradesh, India (falling between the rivers Giri and Tons; both tributaries of the Yamuna River) was studied for landslide vulnerability on behalf of the inhabitants. The study was made using extensive remote sensing data (satellite and airborne). It is well supported by field evidence, demographic and infrastructural details and aided by Geographic Information System (GIS) based techniques. Field observations testify that slope, aspect, geology, tectonic planes, drainage, and land use all influence landslides in the region. These parameters were taken into consideration using the statistical approach of landslide hazard zonation. Using the census data of 1991, vulnerability of the populace to the landslide hazard was accessed. As most of the infrastructure in the region is concentrated around population centres, population data alone was used for vulnerability studies. 相似文献