Application of GIS-based statistical modeling for landslide susceptibility mapping in the city of Azazga,Northern Algeria

Landslide susceptibility mapping is a necessary tool in order to manage the landslides hazard and improve the risk mitigation. In this research, we validate and compare the landslide susceptibility maps (LSMs) produced by applying four geographic information system (GIS)-based statistical approaches including frequency ratio (FR), statistical index (SI), weights of evidence (WoE), and logistic regression (LR) for the urban area of Azazga. For this purpose, firstly, a landslide inventory map was prepared from aerial photographs and high-resolution satellite imagery interpretation, and detailed fieldwork. Seventy percent of the mapped landslides were selected for landslide susceptibility modeling, and the remaining (30%) were used for model validation. Secondly, ten landslide factors including the slope, aspect, altitude, land use, lithology, precipitation, distance to drainage, distance to faults, distance to lineaments, and distance to roads have been derived from high-resolution Alsat 2A satellite images, aerial photographs, geological map, DEM, and rainfall database. Thirdly, we established LSMs by evaluating the relationships between the detected landslide locations and the ten landslides factors using FR, SI, LR, and WoE models in GIS. Finally, the obtained LSMs of the four models have been validated using the receiver operating characteristics curves (ROCs). The validation process indicated that the FR method provided more accurate prediction (78.4%) in generating LSMs than the SI (78.1%),WoE (73.5%), and LR (72.1%) models. The results revealed also that all the used statistical models provided good accuracy in landslide susceptibility mapping.

     

GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar,Turkey)

The case study presents GIS-aided statistically and physically based landslide susceptibility mapping in the landslide-prone Avutmus district of Sebinkarahisar (Giresun, Turkey). Field investigations, analysis of geological data and laboratory tests suggested that two important factors have acted together to cause sliding: ground water pressures and toe erosion. Frequency ratio (FR) and stability index mapping (SINMAP) were used to create the landslide susceptibility maps based on a landslide inventory; distance from drainage systems, faults and roads; slope angle and aspect; topographic elevation and topographical wetness index; and vegetation cover. Validation of the models indicated high quality susceptibility maps with the more realistic results were obtained from the statistically based FR model.     

A comparative assessment of information value,frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed,India

This research work presents a comparative performance of geographic information system (GIS)-based statistical models for landslide susceptibility mapping (LSM) of the Himalayan watershed in India. A total of 190 landslide locations covering an area of 14.63 km² were identified in the watershed, using high-resolution linear imaging self-scanning (LISS IV) data. The causative factors used for LSM of the study area are slope, aspect, lithology, curvature, lineament density, land cover and drainage buffer. The spatial database has been prepared using remote sensing data along with ancillary data like geological maps. LSMs were prepared using information value (InV), frequency ratio (FR) and analytical hierarchy process (AHP) models. The validation results using the prediction rate curve technique show 89.61%, 87.12% and 88.26% area under curve values for FR, AHP and InV models, respectively. Therefore, the frequency ratio (FR) model could be used for LSM in other parts of this hilly terrain.

     

Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression,naive Bayes and RBFNetwork models for the Long County area (China)

The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results show that all three models exhibit reasonably good performance, and the KLR model exhibits the most stable and best performance. The KLR model, which has a success rate of 0.847 and a prediction rate of 0.749, is a promising technique for landslide susceptibility mapping. Given the outcomes of the study, all three models could be used efficiently for landslide susceptibility analysis.

     

Evaluating landslide susceptibility based on cluster analysis,probabilistic methods,and artificial neural networks

In this study, the cluster analysis (CA), probabilistic methods, and artificial neural networks (ANNs) are used to predict landslide susceptibility. The Geographic Information System (GIS) is used as the basic tool for spatial data management. CA is applied to select non-landslide dataset for later analysis. A probabilistic method is suggested to calculate the rating of the relative importance of each class belonging to each conditional factor. ANN is applied to calculate the weight (i.e., relative importance) of each factor. Using the ratings and the weights, it is proposed to calculate the landslide susceptibility index (LSI) for each pixel in the study area. The obtained LSI values can then be used to construct the landslide susceptibility map. The aforementioned proposed method was applied to the Longfeng town, a landslide-prone area in Hubei province, China. The following eight conditional factors were selected: lithology, slope angle, distance to stream/reservoir, distance to road, stream power index (SPI), altitude, curvature, and slope aspect. To assess the conditional factor effects, the weights were calculated for four cases, using 8 factors, 6 factors, 5 factors, and 4 factors, respectively. Then, the results of the landslide susceptibility analysis for these four cases, with and without weighting, were obtained. To validate the process, the receiver operating characteristics (ROC) curve and the area under the curve (AUC) were applied. In addition, the results were compared with the existing landslide locations. The validation results showed good agreement between the existing landslides and the computed susceptibility maps. The results with weighting were found to be better than that without weighting. The best accuracy was obtained for the case with 5 conditional factors with weighting.

     

Spatial data analysis and application of evidential belief functions to shallow landslide susceptibility mapping at Mt. Umyeon,Seoul, Korea

Predictive mapping of landslide occurrence at the regional scale was performed at Mt. Umyeon, in the southern part of Seoul, Korea, using an evidential belief function (EBF) model. To generate the landslide susceptibility map, approximately 90 % of 163 actual landslide locations were randomly selected as a training set, and about 10 % of them were used as a validation set. Spatial data sets relevant to landslide occurrence (topographic factors, hydrologic factors, forest factors, soil factors, and geologic factors) were analyzed in a geographic information system environment. In this study, landslide susceptibility was assessed on the basis of mass function assignment (belief, disbelief, uncertainty, and plausibility) and integration within a data-driven approach. The most representative of the resulting integrated susceptibility maps (the belief map) was validated using the receiver operating characteristic (ROC) method. The verification result showed that the model had an accuracy of 74.3 % and a predictive accuracy of 88.1 %. The frequency ratio (FR) model was also used for comparison with the EBF model. Prediction and success rates of 72.1 and 85.9 % were achieved using the FR model. The validation results showed satisfactory agreement between the susceptibility map and the existing landslide location data. The EBF model was more accurate than the FR model for landslide prediction in the study area. The results of this study can be used to mitigate landslide-induced hazards and for land-use planning.     

Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms

This study aims to investigate the performances of different training algorithms used for an artificial neural network (ANN) method to produce landslide susceptibility maps. For this purpose, Ovacık region (southeast of Karabük Province), located in the Western Black Sea Region (Turkey), was selected as the study area. A total of 196 landslides were mapped, and a landslide database was prepared. Topographical elevation, slope angle, aspect, wetness index, lithology, and vegetation index parameters were taken into account for the landslide susceptibility analyses. Two different ANN structures, which were composed of single and double hidden layers, were applied to compare the effects of the ANN. Four different training algorithms, namely batch back-propagation, quick propagation, conjugate gradient descent (CGD), and Levenberg–Marquardt, were used for the training stage of the ANN models. Thus, eight different landslide susceptibility maps were produced for the study area using different ANN structures and algorithms. In order to assess the effects and spatial performances of the considered training algorithms on the ANN models, the relative operating characteristics (ROC) and relation value (r_ij) approaches were used. The susceptibility map produced by CGD1 has the highest AUC (0.817) and r_ij values (0.972). Comparison of the susceptibility maps indicated that CGD training algorithm is the slowest one among the other algorithms, but this algorithm showed the highest performance on the results.

     

A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran)

Mass movements are among the most dangerous natural hazards in mountainous regions. The present study employs machine learning (ML) models for mass movement susceptibility mapping (MMSM) in Iran based on a comprehensive dataset of 864 mass movements which include debris flow, landslide, and rockfall during the last 42 years (1977–2019) as well as 12 conditional factors. The results of validation stage show that RF (random forest) is the most viable model for mass movement susceptibility maps. In addition, MARS (multivariate adaptive regression splines), MDA (mixture discriminant additive), and BRT (boosted regression trees) models also provide relatively accurate results. Results of the AUC for validation of produced maps were 0.968, 0.845, 0.828, and 0.765 for RF, MARS, MDA, and BRT, respectively. Based on MMSM generated by RF model, 32% of study area is identified to be under high and very high susceptibility classes. Most of the endangered areas for mass movement are in the west and central parts of the Chaharmahal and Bakhtiari Province. In addition, our findings indicate that elevation, slope angle, distance from roads, and distance from faults are critical factors for mass movement. Our results provide a perspective view for decision makers to mitigate natural hazards.

     

Landslide susceptibility mapping of the Sea to Sky transportation corridor, British Columbia, Canada: comparison of two methods

The Sea to Sky corridor stretches over a distance of 135?km into British Columbia’s Coast Mountains. The corridor has witnessed hundreds of historical and pre-historic landslides. In the last 154?years, 155 landslide events have been reported. The most common types of landslides are rockfalls and debris flows, which are small in volume, but can be quite damaging. These are more abundant in the southern part of the corridor where infrastructure is built close to steep slopes. Two different methods were adapted to create debris flow and rockfall/rock slide susceptibility maps. Both qualitative heuristic and fuzzy logic susceptibility maps showed a similar distribution of susceptibility zones, especially high susceptibility. Correlation of high susceptibility zones with occurrence of historical and mapped geological landslide events was very good. Success rate curves were calculated for extrapolated zones of initiation for debris flow and rockfall/rock slide deposits. Success rate curves were better for debris flow than rockfall/rockslide maps.     

A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks

A case study for the use of an artificial neural network (ANN) model for landslide susceptibility mapping in Koyulhisar (Sivas-Turkey) is presented. Digital elevation model (DEM) was first constructed using ArcGIS software. Relevant parameter maps were created, including geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index and distance from roads. Finally, a landslide susceptibility map was constructed using the neural networks. The drawbacks of the method are discussed but as the validation procedures used confirmed the quality of the map produced, it is recommended the use of ANN may be helpful for planners and engineers in the initial assessment of landslide susceptibility.      

Landslide susceptibility mapping of Blue Nile and Tekeze River Basins using oblique rainfall-aspect rasters

This research was carried out to prepare the regional level landslide susceptibility maps by incorporating the oblique rainfall raster in the upper Blue Nile and Tekeze River basins. The oblique rainfall is the amount that actually falls on sloping surfaces, and varies considerably with slope inclination and aspect with respect to the prevailing trend of the wind direction. The monthly averaged precipitation data for the Kermit (July–September) and the Belg (March–April) rainfall seasons for the study area were acquired for the period of 1950 to 2000, and utilized to compute the oblique rainfall vectors at 40°, 45°, 50°, 55°, and 60° angles (representing “wind-driven” rainfall vectors). The weighted overlay index method using ArcGIS software was applied for this regional landslide susceptibility mapping (scales >1:100,000) by incorporating vertical rainfall intensity maps and aspect separately and as a combination (rainfall raster coupled with the slope aspect raster). The resulting landslide susceptibility maps were compared which reveals that the results obtained from using integrated rainfall/aspect raster’s (combined) were found to be more reasonable towards computing high to very high hazards than using aspect and rainfall rasters as separate layers. The susceptibility maps were validated with landslide inventory maps as well as documented rockslides, scattered throughout the study area. This reconnaissance level study could serve as guide maps in identifying those areas where more detailed landslide hazard mapping might, or should be, undertaken in the future for detailed investigations.     

Assessment and validation of GIS-based landslide susceptibility maps: a case study from Feltrino stream basin (Central Italy)

Landslide susceptibility studies focus on producing susceptibility maps starting from landslide inventories and considering the main conditioning factors. The validity of susceptibility maps must be verified in terms of model accuracy and prediction skills. This paper deals with a GIS-based landslide susceptibility analysis and relative validation in a hilly-coastal test-area in Adriatic Central Italy. The susceptibility analysis was performed via bivariate statistics using the Landslide-Index method and a detailed (field-based) landslide inventory. Selection and mapping of conditioning factors and landslide inventories was derived from detail geomorphological analyses of the study area. The susceptibility map was validated using recent (shallow) landslides in terms of both model accuracy and prediction skills, via Success rate and Prediction rate curves, respectively. In addition, a pre-existing official landslide inventory was applied to the model to test whether it can be used when a detailed (field-based) inventory is not available, thereby extending its usability in similar physiographic regions. The outcome of this study reveals that slope and lithology are the main conditioning factor of landslides, but also highlights the key role of surficial deposits in susceptibility assessment, for both their type and thickness. The validation results show the effectiveness of the susceptibility model in both model accuracy and prediction skills given the good percentage of correctly classified landslides. Moreover, comparison of the susceptibility map with the official Regional landslides inventory proves the possibility of using the developed susceptibility model also in the absence of detailed landslide mapping, by considering inventories that are already available.     

Mapping the susceptibility of rainfall and earthquake triggered landslides along China–Nepal highways

The 2015 Gorkha earthquake (Mw?=?7.8) caused significant earthquake triggered landslides (ETL) in a landscape that is heavily intervened by rainfall triggered landslides (RTL). China’s Belt and Road Initiative plan to boost South-Asian regional trade and mobility through two key highway corridors, i.e. 1) Longmu–Rasuwa–Kathmandu (LRK) and 2) Nyalam–Tatopani–Kathmandu (NTK) route, that dissect the Himalayas through this geologically unstable region. To understand the spatial characteristics and susceptibility of these ETL and RTL, we delineate the landslides by means of time variant satellite imageries, assess their spatial distribution and model their susceptibilities along the highway slopes. We use a coupled frequency ratio (FR) – analytical hierarchy process (AHP) model by considering nine landslide determinants, e.g. geomorphic type (slope, aspect, curvature, elevation), hydrologic type (erosive potential of gullies, i.e. stream power index and distance to streams), normalized difference vegetation index, lithology and civil structure type (i.e. distance to roads). The results demonstrate that elevation and slope predominantly control both these landslide occurrences. The model predicts locations of ETL with higher accuracy than RTL. On comparison, NTK was safer with 133.5 km² of high RTL or ETL (or both) landslide susceptible areas, whereas LRK has 216.04 km². For mapping the extent of these landslides, we constricted it to the slope units of highways to reduce the computational effort, but this technique successfully achieved an acceptable threefold average model prediction rate of 82.75% in ETL and 77.9% in RTL. These landslide susceptibility maps and route comparisons would provide guidance towards further planning, monitoring, and implementing landslide risk mitigation measures for the governments.

     

Rapid susceptibility mapping of earthquake-triggered slope geohazards in Lushan County by combining remote sensing with the AHP model developed for the Wenchuan earthquake

A rapid-response mapping model can be used to study the susceptibility of areas of interest to geohazards (which are commonly regarded as among the most damaging natural hazards), assuming that the model is stable (i.e., that it is generally applicable to any such area). Applying a predefined predictive geohazard-susceptibility model to an area with geoenvironmental conditions similar to those of the area for which it was originally formulated is an effective method of testing the stability of the model. In this paper, the analytic hierarchy process (AHP)-based model developed for the Wenchuan earthquake was used to study susceptibility to earthquake-triggered slope geohazards in Lushan County. Upon integrating the results of a literature review, site investigation, and remote sensing interpretation, seven main factors that influence earthquake-triggered slope geohazards were identified, including peak ground acceleration, distance from a stream, distance from a highway, slope gradient, slope position, normalized difference vegetation index, and micro-landform. In order to reduce the subjectivity of the expert evaluation method usually applied in the AHP, these factors were ranked by relative importance using regression analysis. The weight of each factor was then calculated by the AHP. The susceptibility mapping model was obtained on the ArcGIS platform, utilizing map overlaying. Finally, the results were re-classified to obtain a map of slope geohazard susceptibility. The accuracy of the AHP model was evaluated using both qualitative and quantitative methods. In the qualitative method, the modeled distribution of susceptibility was compared with the actually distribution of geohazards in the study area (identified through remote sensing interpretation), and the areas with high and very high geohazard susceptibilities in the model were found to match well with the actual locations of slope geohazards. In the quantitative method, statistical data showed that over 66% of the geohazards were located in areas of high or very high susceptibility according to the model, while only about 16% were located in areas of very low or low susceptibility, and the density of slope geohazards was about 125 times greater in the areas with very high susceptibility than in the areas with very low susceptibility. Also, the AUC value of the ROC curve for the model suggested that it has high predictive power (a predictive accuracy of 84.8%). In conclusion, it was possible to make accurate predictions about the slope geohazards in earthquake-prone areas located in mountainous regions based on geospatial data, and a high correlation between the susceptibility map generated by the AHP-based model and the true distribution of slope geohazards was observed. Therefore, the AHP-based model used here could be applied to map the slope geohazard susceptibility in other mountainous regions which may be prone to slope geohazards during earthquakes.     

Semi-quantitative method to identify the vulnerable areas in terms of building aggregation for probable landslide runout at the regional scale: a case study from Soacha Province,Colombia

Landslide susceptibility and vulnerability maps are key components for urban planning and risk management. The main objective of this research was spatial vulnerability mapping in the probable landslide runout zone in Soacha Province, Colombia. This study included three major steps: identification of a landslide susceptible area, identification of its runout zone, and vulnerability assessment using an area damage index method. The landslide-prone area was identified through a susceptibility analysis using a logistic regression model. In total, 182 landslide locations were collected and randomly distributed as training data (70%) and validation data (30%). The final landslide susceptibility map was validated using the area under the curve method. The validation result showed success and prediction rates of 88.71% and 89.96%, respectively. The Flow-R model was applied to identify the runout zone, and a back-propagation analysis approach was applied to estimate two essential input data for the model, i.e., the travel angle and velocity. From seven locations, the back-propagation analysis showed an average travel angle of 14.6° and an average velocity of 11.4 m/s. A total of 3777 buildings were identified within the probable runout zone. A physical vulnerability assessment was done by finding the ratio between area of buildings and area of runout zone in each small unit boundary. The physical vulnerability was classified as low, moderate, extensive, and complete on the basis of building exposure. The final result revealed that most of the village areas are in null or moderate vulnerability zones. In contrast to the village areas, the city areas include zones of extensive and complete vulnerability. This study showed that about 52% of the area of the city of Cazuca is completely vulnerable, i.e., in areas where abandoned quarry sites are present. The map of vulnerable areas may assist planners in overall landslide risk management.

     

Landslide hazard assessment and judgment of reliability: a geomechanical approach

Landslide hazard maps are often defined as reliable a posteriori, in accordance with the real landslides occurring from the time of the map production. However, to be useful for planning, a reliability judgment concerning the hazard mapping should be a priori, based on data uncertainty characterization, and must be driven by the knowledge of the slope instability mechanisms. The landslide hazard assessment, when based on the deterministic diagnosis of the processes, may really lead to really providing clues about how and why the slope could fail (landslide susceptibility) and, possibly, when (landslide hazard). Such deterministic assessment can be pursued only through the interpretation and the geo-hydro-mechanical modelling of the slope equilibrium. In practice, though, the landslide hazard assessment is still seldom dealt with slope modelling, in particular when it addresses intermediate to regional zoning. The paper firstly offers an overview of the key steps of a methodology called the multiscalar method for landslide mitigation, MMLM, which that is a methodological approach for the intermediate to regional landslide hazard assessment using the hydro-mechanical diagnoses of landsliding. The validation of the MMLM to the geologically complex outer sectors of the Southern Apennines (Daunia-Lucanian mountains; Italy) is also delineated, together with a practical approach to incorporate a reliability judgment in the landslide susceptibility/hazard mapping.     

Landslide assessment for land use planning and infrastructure management in the Paphos District of Cyprus

The Paphos District has been described as one of the most landslide-prone areas of Cyprus, with landslides impacting villages, roads and other infrastructure. With increasing levels of development and investment in infrastructure, Cypriot authorities are investigating ways to assess landslide susceptibility, hazard and risk for planning purposes. A 2-year project has catalogued over 1,840 landslides, investigated the spatial distribution of key landslide attributes, and used the results to develop maps of landslide susceptibility across large areas of the Paphos District. To gain a better understanding of the materials and failure mechanisms involved, 20 of these landslides were selected for further study, including engineering geological mapping, ground investigation, laboratory testing, development of ground models and slope stability analysis at specific locations. The results enabled soil parameters to be reviewed, thus strengthening the interpretations derived from field observations. The use of the mapping outputs is discussed in terms of planning and engineering applications and recommendations are made for strengthening and expanding the landslide database.     

Identification of landslide hazard and risk ‘hotspots’ in Europe

Landslides are a serious problem for humans and infrastructure in many parts of Europe. Experts know to a certain degree which parts of the continent are most exposed to landslide hazard. Nevertheless, neither the geographical location of previous landslide events nor knowledge of locations with high landslide hazard necessarily point out the areas with highest landslide risk. In addition, landslides often occur unexpectedly and the decisions on where investments should be made to manage and mitigate future events are based on the need to demonstrate action and political will. The goal of this study was to undertake a uniform and objective analysis of landslide hazard and risk for Europe. Two independent models, an expert-based or heuristic and a statistical model (logistic regression), were developed to assess the landslide hazard. Both models are based on applying an appropriate combination of the parameters representing susceptibility factors (slope, lithology, soil moisture, vegetation cover and other- factors if available) and triggering factors (extreme precipitation and seismicity). The weights of different susceptibility and triggering factors are calibrated to the information available in landslide inventories and physical processes. The analysis is based on uniform gridded data for Europe with a pixel resolution of roughly 30 m × 30 m. A validation of the two hazard models by organizations in Scotland, Italy, and Romania showed good agreement for shallow landslides and rockfalls, but the hazard models fail to cover areas with slow moving landslides. In general, the results from the two models agree well pointing out the same countries with the highest total and relative area exposed to landslides. Landslide risk was quantified by counting the number of exposed people and exposed kilometers of roads and railways in each country. This process was repeated for both models. The results show the highest relative exposure to landslides in small alpine countries such as Lichtenstein. In terms of total values on a national level, Italy scores highest in both the extent of exposed area and the number for exposed population. Again, results agree between the two models, but differences between the models are higher for the risk than for the hazard results. The analysis gives a good overview of the landslide hazard and risk hotspots in Europe and allows a simple ranking of areas where mitigation measures might be most effective.     

Landslide-susceptibility mapping in a semi-arid mountain environment: an example from the southern slopes of Sierra Nevada (Granada,Spain)

Landslide susceptibility is analysed in a semi-arid mountain environment, on the southern slope of Sierra Nevada. In a study area of 460 km², 252 landslides were inventoried, affecting 3.2% of the total surface area. These landslides were mainly slides and flows on phyllite, schist and marble units in the Inner Zone of the Betic Cordillera. The most relevant determining factors proved to be elevation, slope angle, slope aspect and lithology. Triggering factors include mainly short-term landslide generation during heavy rainfall, as well as sporadic earthquakes or long-term activation by land-use changes, river over-excavation, etc. Although landslide susceptibility, assessed by the GIS matrix method, is predominantly low, some 15% of the study area shows moderate to very high susceptibility, coinciding with the sites of public works in the region. The map drawn was validated by the degree-of-fit method, registering values above 83.2% for the zones of high and very high susceptibility.     

证据权法在滑坡危险度区划研究中的应用

采用证据权法对长江三峡库区秭归--巴东段进行了滑坡危险度区划研究。数据源主要包括地质图、SPOT5多光谱卫星影像数据及数字高程模型(DEM)等。利用RS和GIS的数据提取和分析功能,分别提取了地质(岩性和构造)、地形(坡度、坡向、水系、高程、沟壑缓冲区、沟壑密度)、水文地质(植被指数)和破坏动力(河流缓冲区)等对研究区滑坡发育影响较大的影响因子的信息。将上述各种影响因子进行分级,建立若干证据层;然后根据已知滑坡在不同证据层中的分布,确定相应证据层的权重值;最后根据不同影响因子权重值的叠加来确定具体某一个单元滑坡发育的概率。分析结果与现有滑坡的分布情况比较吻合。采用证据权法可以客观定量地评价各种影响因子对滑坡发育的影响程度,并据此进行滑坡危险度评价因子(证据因子)的选择及危险度定量评价。