A fuzzy set based approach for integration of thematic maps for landslide susceptibility zonation

Spatial prediction of landslides is termed landslide susceptibility zonation (LSZ). In this study, an objective weighting approach based on fuzzy concepts is used for LSZ in a part of the Darjeeling Himalayas. Relevant thematic layers pertaining to landslide causative factors have been generated using remote sensing and geographic information system (GIS) techniques. The membership values for each category of thematic layers have been determined using the cosine amplitude fuzzy similarity method and are used as ratings. The integration of these ratings led to the generation of LSZ map. The integration of different ratings to generate an LSZ map has been performed using a fuzzy gamma operator apart from the arithmetic overlay approach. The process is based on determination of combined rating known as the landslide susceptibility index (LSI) for all the pixels using the fuzzy gamma operator and classification using the success rate curve method to prepare the LSZ map. The results indicate that as the gamma value increases, the accuracy of the LSZ map also increases. It is observed that the LSZ map produced by the fuzzy algebraic sum has reflected a more real situation in terms of landslides in the study area.     

A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas

Landslides are one of the most destructive phenomena of nature that cause damage to both property and life every year, and therefore, landslide susceptibility zonation (LSZ) is necessary for planning future developmental activities. In this paper, apart from conventional weighting system, objective weight assignment procedures based on techniques such as artificial neural network (ANN), fuzzy set theory and combined neural and fuzzy set theory have been assessed for preparation of LSZ maps in a part of the Darjeeling Himalayas. Relevant thematic layers pertaining to the causative factors have been generated using remote sensing data, field surveys and Geographic Information System (GIS) tools. In conventional weighting system, weights and ratings to the causative factors and their categories are assigned based on the experience and knowledge of experts about the subject and the study area to prepare the LSZ map (designated here as Map I). In the context of objective weight assignments, initially the ANN as the black box approach has been used to directly produce an LSZ map (Map II). In this approach, however, the weights assigned are hidden to the analyst. Next, the fuzzy set theory has then been implemented to determine the membership values for each category of the thematic layer using the cosine amplitude method (similarity method). These memberships are used as ratings for each category of the thematic layer. Assuming weights of each thematic layer as one (or constant), these ratings of the categories are used for the generation of another LSZ map (Map III). Subsequently, a novel weight assignment procedure based on ANN is implemented to assign the weights to each thematic layer objectively. Finally, weights of each thematic layer are combined with fuzzy set derived ratings to produce another LSZ map (Map IV). The maps I–IV have been evaluated statistically based on field data of existing landslides. Amongst all the procedures, the LSZ map based on combined neural and fuzzy weighting (i.e., Map IV) has been found to be significantly better than others, as in this case only 2.3% of the total area is found to be categorized as very high susceptibility zone and contains 30.1% of the existing landslide area.     

An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas

Landslide susceptibility zonation (LSZ) is necessary for disaster management and planning development activities in mountainous regions. A number of methods, viz. landslide distribution, qualitative, statistical and distribution-free analyses have been used for the LSZ studies and they are again briefly reviewed here. In this work, two methods, the Information Value (InfoVal) and the Landslide Nominal Susceptibility Factor (LNSF) methods that are based on bivariate statistical analysis have been applied for LSZ mapping in a part of the Himalayas. Relevant thematic maps representing various factors (e.g., slope, aspect, relative relief, lithology, buffer zones along thrusts, faults and lineaments, drainage density and landcover) that are related to landslide activity, have been generated using remote sensing and GIS techniques. The LSZ derived from the LNSF method, has been compared with that produced from the InfoVal method and the result shows a more realistic LSZ map from the LNSF method which appears to conform to the heterogeneity of the terrain.     

Identification of landslide susceptibility zones in Gish River basin,West Bengal,India

The occurrence of landslide in the hilly region of Darjeeling during monsoon season is a matter of serious concern. Every year this natural hazard damages the major roads at several places and thus disrupts the transport and communication system in this region. This paper tries to prepare a landslide susceptibility zone (LSZ) map for the Gish River basin. A total number of 16 spatial parameters have been taken for this study and these are categorised under six factor clusters or groups for example, triggering factors, protective factor, lithological factors, morphometric factors, hydrological factors and anthropogenic factors. The LSZ map is prepared by integrating all the parameters adopting the weighting base as logistic regression. The landslide susceptibility map shows that nearly 9.11% of the area falls under the very high landslide-susceptible zone while 40.28% of the area of the total basin lies under the very low landslide-susceptible zone. The landslide-susceptible model is validated through the receiver operating characteristic curve. This curve shows 86% success rate in defining landslide-susceptible zones and 83.40% prediction rate for the occurrence of landslides. The spatial relationship between the landslide susceptibility model and other factors’ groups shows that the morphometric factors’ cluster (mainly slope) is the focalone for the determination of landslide-susceptible zone.     

Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas

Landslide risk assessment (LRA) is a key component of landslide studies. The landslide risk can be defined as the potential for adverse consequences or loss to human population and property due to the occurrence of landslides. The LRA can be regional or site-specific in nature and is an important information for planning various developmental activities in the area. LRA is considered as a function of landslide potential (LP) and resource damage potential (RDP). The LP and RDP are typically characterized by the landslide susceptibility zonation map and the resource map (i.e., land use land cover map) of the area, respectively. Development of approaches for LRA has always been a challenge. In the present study, two approaches for LRA, one based on the concept of danger pixels and the other based on fuzzy set theory, have been developed and implemented to generate LRA maps of Darjeeling Himalayas, India. The LRA map based on the first approach indicates that 1,015 pixels of habitation and 921 pixels of road section are under risk due to landslides. The LRA map derived from fuzzy set theory based approach shows that a part of habitat area (2,496 pixels) is under very high risk due to landslides. Also, another part of habitat area and a portion of road network (7,204 pixels) are under high risk due to landslides. Thus, LRA map based on the concept of danger pixels gives the pixels under different resource categories at risk due to landslides whereas the LRA map based on the concept of fuzzy set theory further refines this result by defining the degree of severity of risk to these categories by putting these into high and low risk zones. Hence, the landslide risk assessment study carried out using two approaches in this paper can be considered in cohesion for assessing the risks due to landslides in a region.     

Application and verification of fuzzy algebraic operators to landslide susceptibility mapping

The aim of this study was to apply and to verify the use of fuzzy logic to landslide susceptibility mapping in the Gangneung area, Korea, using a geographic information system (GIS). For this aim, in the study, a data-derived model (frequency ratio) and a knowledge-derived model (fuzzy operator) were combined. Landslide locations were identified by changing the detection technique of KOMPSAT-1 images and checked by field studies. For landslide susceptibility mapping, maps of the topography, lineaments, soil, forest, and land cover were extracted from the spatial data sets, and the eight factors influencing landslide occurrence were obtained from the database. Using the factors and the identified landslide, the fuzzy membership values were calculated. Then fuzzy algebraic operators were applied to the fuzzy membership values for landslide susceptibility mapping. Finally, the produced map was verified by comparing with existing landslide locations for calculating prediction accuracy. Among the fuzzy operators, in the case in which the gamma operator (λ = 0.975) showed the best accuracy (84.68%) while the case in which the fuzzy or operator was applied showed the worst accuracy (66.50%).     

A quantitative approach for improving the BIS (Indian) method of medium-scale landslide susceptibility

In India, the Bureau of Indian Standards (BIS) recommends a heuristic method for medium-scale (1:25,000/1:50,000) landslide susceptibility mapping. This is based on fixed ratings of geofactors, without the inclusion of landslide inventory information. In BIS method, the pre-defined ratings of geofactors are applied over diverse areas, irrespective of the terrain-specific spatial inter-dependence of geofactors and landslide types, which leads to rather moderate prediction. In this paper, we evaluate the effectiveness of the existing BIS method in Darjeeling Himalaya through a quantitative method adapting weights of evidence (WofE) modeling. The quantified spatial associations between specific geofactors for different landslide types and failure mechanisms that were generated, using this method showed improved prediction rates as compared to the BIS method of fixed ratings of geofactors. We therefore recommend adjusting the existing BIS guidelines by inclusions of weights, derived locally through quantitative spatial analysis of landslide inventories and geofactor maps.     

Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia

Landslides are one of the most frequent and common natural hazards in Malaysia. Preparation of landslide susceptibility maps is one of the first and most important steps in the landslide hazard mitigation. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, a number of different approaches have been used, including direct and indirect heuristic approaches, deterministic, probabilistic, statistical, and data mining approaches. Moreover, these landslides can be systematically assessed and mapped through a traditional mapping framework using geoinformation technologies. Since the early 1990s, several mathematical models have been developed and applied to landslide hazard mapping using geographic information system (GIS). Among various approaches, fuzzy logic relation for mapping landslide susceptibility is one of the techniques that allows to describe the role of each predisposing factor (landslide-conditioning parameters) and their optimal combination. This paper presents a new attempt at landslide susceptibility mapping using fuzzy logic relations and their cross application of membership values to three study areas in Malaysia using a GIS. The possibility of capturing the judgment and the modeling of conditioning factors are the main advantages of using fuzzy logic. These models are capable to capture the conditioning factors directly affecting the landslides and also the inter-relationship among them. In the first stage of the study, a landslide inventory was complied for each of the three study areas using both field surveys and airphoto studies. Using total 12 topographic and lithological variables, landslide susceptibility models were developed using the fuzzy logic approach. Then the landslide inventory and the parameter maps were analyzed together using the fuzzy relations and the landslide susceptibility maps produced. Finally, the prediction performance of the susceptibility maps was checked by considering field-verified landslide locations in the studied areas. Further, the susceptibility maps were validated using the receiver-operating characteristics (ROC) success rate curves. The ROC curve technique is based on plotting model sensitivity—true positive fraction values calculated for different threshold values versus model specificity—true negative fraction values on a graph. The ROC curves were calculated for the landslide susceptibility maps obtained from the application and cross application of fuzzy logic relations. Qualitatively, the produced landslide susceptibility maps showed greater than 82% landslide susceptibility in all nine cases. The results indicated that, when compared with the landslide susceptibility maps, the landslides identified in the study areas were found to be located in the very high and high susceptibility zones. This shows that as far as the performance of the fuzzy logic relation approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.     

Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method

A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative or quantitative approaches. The present study is an attempt to utilise a multivariate statistical method called binary logistic regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India, lying close to the Main Boundary Thrust (MBT). This method gives the freedom to use categorical and continuous predictor variables together in a regression analysis. Geographic Information System has been used for preparing the database on causal factors of slope instability and landslide locations as well as for carrying out the spatial modelling of landslide susceptibility. A forward stepwise logistic regression analysis using maximum likelihood estimation method has been used in the regression. The constant and the coefficients of the predictor variables retained by the regression model have been used to calculate the probability of slope failure for the entire study area. The predictive logistic regression model has been validated by receiver operating characteristic curve analysis, which has given 91.7% accuracy for the developed BLR model.     

Landslide susceptibility assessment using Information Value Method in parts of the Darjeeling Himalayas

Landslide susceptibility is the likelihood of a landslide occurrence in an area predicted on the basis of local terrain conditions. Since last few years, researchers have attempted to analyse the probability of landslide occurrences and introduced different methods of landslide susceptibility assessment. The objective of this paper is to assess the landslide susceptibility in parts of the Darjeeling Himalayas using a relatively simple bivariate statistical technique. Seven factor layers with 24 categories, responsible for landslide occurrences in this area, are prepared from Cartosat and Resourcesat — 1 LISS-IV MX data. Each category was given a weight using the Information Value Method. Weighted sum of these values were used to prepare a landslide susceptibility map. The result shows that 8% area was predicted for high, 32% for moderate and remaining 60% for low landslide susceptibility zones. The high value (0.89) of the area under the receiver operating characteristic curve showed the high accuracy of the prediction model.     

Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region,Tehran, Iran

Landslide susceptibility mapping is among the useful tools applied in disaster management and planning development activities in mountainous areas. The susceptibility maps prepared in this research provide valuable information for landslide hazard management in Lashgarak region of Tehran. This study was conducted to, first, prepare landslide susceptibility maps for Lashgarak region and evaluate landslide effect on mainlines and, second, to analyze the main factors affecting landslide hazard increase in the study area in order to propose efficient strategies for landslide hazard mitigation. A GIS-based multi-criteria decision analysis model (fuzzy logic) is used in the present work for scientific evaluation of landslide susceptible areas in Lashgarak region. To this end, ArcGIS, PCIGeomatica, and IDIRISI software packages were used. Eight information layers were selected for information analysis: ground strength class, slope angle, terrain roughness, normalized difference moisture index, normalized difference vegetation index, distance from fault, distance from the river, and distance from the road. Next, eight different scenarios were created to determine landslide susceptibility of the study area using different operators (intersection (AND), union (OR), algebraic sum (SUM), multiplication (PRODUCT), and different fuzzy gamma values) of fuzzy overlay approach. After that, the performance of various fuzzy operators in landslide susceptibility mapping was empirically compared. The results revealed the excellent consistency of landslide susceptibility map prepared using the fuzzy union (OR) operator with landslide distribution map in the study area. Eventually, the accuracy of landslide susceptibility map prepared using the fuzzy union (OR) operator was evaluated using the frequency ratio diagram. The results showed that frequency values of the landslides gradually increase from “low susceptibility” to high “susceptibility” as 88.34% of the landslides are categorized into two “high” and “very high” susceptibility classes, implying the satisfactory consistency between the landslide susceptibility map prepared using fuzzy union (OR) operator and landslide distribution map.     

Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey)

Preparation of landslide susceptibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, many procedures have been used to produce such maps. In this study, a new attempt is tried to produce landslide susceptibility map of a part of West Black Sea Region of Turkey. To obtain the fuzzy relations for producing the susceptibility map, a landslide inventory database is compiled by both field surveys and airphoto studies. A total of 266 landslides are identified in the study area, and dominant mode of failure is rotational slide while the other mode of failures are soil flow and shallow translational slide. The landslide inventory and the parameter maps are analyzed together using a computer program (FULLSA) developed in this study. The computer program utilizes the fuzzy relations and produces the landslide susceptibility map automatically. According to this map, 9.6% of the study area is classified as very high susceptibility, 10.3% as high susceptibility, 8.9% as moderate susceptibility, 27.5% as low susceptibility and 43.8% as very low susceptibility or nonsusceptible areas. The prediction performance of the susceptibility map is checked by considering actual landslides in the study area. For this purpose, strength of the relation (r_ij) and the root mean square error (RMSE) values are calculated as 0.867 and 0.284, respectively. These values show that the produced landslide susceptibility map in the present study has a sufficient reliability. It is believed that the approach employed in this study mainly prevents the subjectivity sourced from the parameter selection and provides a support to improve the landslide susceptibility mapping studies.     

Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya

This study considers landslide susceptibility mapping by means of frequency ratio and artificial neural network approaches using geographic information system (GIS) techniques as a basic analysis tool. The selected study area was that of the Panchthar district, Nepal. GIS was used for the management and manipulation of spatial data. Landslide locations were identified from field survey and aerial photographic interpretation was used for location of lineaments. Ten factors in total are related to the occurrence of landslides. Based on the same set of factors, landslide susceptibility maps were produced from frequency ratio and neural network models, and were then compared and evaluated. The weights of each factor were determined using the back-propagation training method. Landslide susceptibility maps were produced from frequency ratio and neural network models, and they were then compared by means of their checking. The landslide location data were used for checking the results with the landslide susceptibility maps. The accuracy of the landslide susceptibility maps produced by the frequency ratio and neural networks is 82.21 and 78.25%, respectively.     

GIS based landslide susceptibility mapping using a fuzzy logic approach: A case study from Ghurmi-Dhad Khola area, Eastern Nepal

Landslides cause extensive loss of life and property in the Nepal Himalaya. Since the late 1980s, different mathematical models have been developed and applied for landslide susceptibility mapping and hazard assessment in Nepal. The main goal of this paper is to apply fuzzy logic to landslide susceptibility mapping in the Ghurmi-Dhad Khola area, Eastern Nepal. Seven causative factors are considered: slope angle, slope aspect, distance from drainage, land use, geology, distance from faults and folds, soil and rock type. Likelihood ratios are obtained for each class of causative factors by comparison with past landslide occurrences. The ratios are normalized between zero and one to obtain fuzzy membership values. Further, different fuzzy operators are applied to generate landslide susceptibility maps. Comparison with the landslide inventory map reveals that the fuzzy gamma operator with a γ-value of 0.60 yields the best prediction accuracy. Consequently, this operator is used to produce the final landslide susceptibility zonation map.     

Macro-zonation of landslide susceptibility in Garamaura-Swarghat-Gambhar section of national highway 21, Bilaspur District, Himachal Pradesh (India)

Landslides the most common geo-hazard in hilly terrain are short lived phenomena but cause extraordinary landscape changes and destruction of life and property. The frequency and intensity of landslides occurrences along NH-21 during the rainy season not only disrupts traffic movement but also misbalance the agro-economic and developmental activities of the region frittering away thousand crores of rupees from the exchequer. An assessment of landslide susceptibility is, therefore, a prerequisite for sustainable development of the region. The present study deals with the preparation of macro-zonation maps of landslide susceptibility in an area of about 100 sq km on 1:50,000 scale across Garamaura-Swarghat section of National Highway-21. The map has been prepared by superimposing the terrain evaluation maps in a particular zone such as lithological map, structural map, slope morphometry map, relative relief map, land use and land cover map and hydrological condition map using landslide susceptibility evaluation factor rating scheme and calculating the total estimated susceptibility as per the guidelines of IS: 14496 (Part-2) 1998). Numerical weightages are assigned to the prime causative factors of slope instability such as lithology, structure, slope morphometery, relative relief, land use and groundwater conditions as per the scheme approved by Bureau of Indian Standard for the purpose of landslide susceptibility zonation. The area depicts zones of different instability. The identified susceptibility zones compared with landslide intensity in the area show some congruence with the weightages of the inputs. The incongruence in intensity and frequency of landslide occurrences and the inferred susceptibility zones of BIS scheme allow other geotechnical considerations and causative factors to be incorporated for the landslide susceptibility zonation.     

Quantitative landslide susceptibility mapping at Pemalang area,Indonesia

For quantitative landslide susceptibility mapping, this study applied and verified a frequency ratio, logistic regression, and artificial neural network models to Pemalang area, Indonesia, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of aerial photographs, satellite imagery, and field surveys; a spatial database was constructed from topographic and geological maps. The factors that influence landslide occurrence, such as slope gradient, slope aspect, curvature of topography, and distance from stream, were calculated from the topographic database. Lithology was extracted and calculated from geologic database. Using these factors, landslide susceptibility indexes were calculated by frequency ratio, logistic regression, and artificial neural network models. Then the landslide susceptibility maps were verified and compared with known landslide locations. The logistic regression model (accuracy 87.36%) had higher prediction accuracy than the frequency ratio (85.60%) and artificial neural network (81.70%) models. The models can be used to reduce hazards associated with landslides and to land-use planning.     

Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu,Turkey)

Landslides and their assessments are of great importance since they damage properties, infrastructures, environment, lives and so on. Particularly, landslide inventory, susceptibility, and hazard or risk mapping have become important issues in the last few decades. Such maps provide useful information and can be produced by qualitative or quantitative methods. The work presented in this paper aimed to assess landslide susceptibility in a selected area, covering 570.625 km² in the Western Black Sea region of Turkey, by two quantitative methods. For this purpose, in the first stage, a detailed landslide inventory map was prepared by extensive field studies. A total of 96 landslides were mapped during these studies. To perform landslide susceptibility analyses, six input parameters such as topographical elevation, lithology, land use, slope, aspect and distance to streams were considered. Two quantitative methods, logistic regression and fuzzy approach, were used to assess landslide susceptibility in the selected area. For the fuzzy approach, the fuzzy and, or, algebraic product, algebraic sum and gamma operators were considered. At the final stage, 18 landslide susceptibility maps were produced by the logistic regression and fuzzy operators in a GIS (Geographic Information System) environment. Two performance indicators such as ROC (relative operating characteristics) and cosine amplitude method (r _ij ) were used to validate the final susceptibility maps. Based on the analyses, the landslide susceptibility map produced by the fuzzy gamma operator with a level of 0.975 showed the best performance. In addition, the maps produced by the logistic regression, fuzzy algebraic product and the higher levels of gamma operators showed more satisfactory results, while the fuzzy and, or, algebraic sum maps were not sufficient to provide reliable outputs.     

Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya,India

Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world. The number of landslides and the level of damage across the globe has been increasing over time. Therefore, landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region. Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study. The prime goal of the study is to prepare landslide susceptibility maps(LSMs) using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide, twenty factors, including triggering and causative factors, were selected. A deep learning algorithm viz. convolutional neural network model(CNN) and three popular machine learning techniques, i.e., random forest model(RF), artificial neural network model(ANN), and bagging model, were employed to prepare the LSMs. Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points. A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor, and the role of individual conditioning factors was estimated using information gain ratio. The result reveals that there is no severe multicollinearity among the landslide conditioning factors, and the triggering factor rainfall appeared as the leading cause of the landslide. Based on the final prediction values of each model, LSM was constructed and successfully portioned into five distinct classes, like very low, low, moderate, high, and very high susceptibility. The susceptibility class-wise distribution of landslides shows that more than 90% of the landslide area falls under higher landslide susceptibility grades. The precision of models was examined using the area under the curve(AUC) of the receiver operating characteristics(ROC) curve and statistical methods like root mean square error(RMSE) and mean absolute error(MAE). In both datasets(training and validation), the CNN model achieved the maximum AUC value of 0.903 and 0.939, respectively. The lowest value of RMSE and MAE also reveals the better performance of the CNN model. So, it can be concluded that all the models have performed well, but the CNN model has outperformed the other models in terms of precision.     

Rainfall event-based landslide susceptibility zonation mapping

Landslide susceptibility assessment is a major research topic in geo-disaster management. In recent days, various landslide susceptibility and landslide hazard assessment methodologies have been introduced with diverse thoughts of assessment and validation method. Fundamentally, in landslide susceptibility zonation mapping, the susceptibility predictions are generally made in terms of likelihoods and probabilities. An overview of landslide susceptibility zoning practices in the last few years reveals that susceptibility maps have been prepared to have different accuracies and reliabilities. To address this issue, the work in this paper focuses on extreme event-based landslide susceptibility zonation mapping and its evaluation. An ideal terrain of northern Shikoku, Japan, was selected in this study for modeling and event-based landslide susceptibility mapping. Both bivariate and multivariate approaches were considered for the zonation mapping. Two event-based landslide databases were used for the susceptibility analysis, while a relatively new third event landslide database was used in validation. Different event-based susceptibility zonation maps were merged and rectified to prepare a final susceptibility zonation map, which was found to have an accuracy of more than 77 %. The multivariate approach was ascertained to yield a better prediction rate. From this study, it is understood that rectification of susceptibility zonation map is appropriate and reliable when multiple event-based landslide database is available for the same area. The analytical results lead to a significant understanding of improvement in bivariate and multivariate approaches as well as the success rate and prediction rate of the susceptibility maps.     

滑坡灾害空间预测支持向量机模型及其应用

随着GIS技术在滑坡灾害空间预测研究中的广泛应用,滑坡灾害空间预测模型成为研究的热点问题。在总结滑坡灾害空间预测研究现状的基础上,简要介绍了两类和单类支持向量机的基本原理。以香港自然滑坡空间预测为例,采用两类和单类支持向量机进行滑坡灾害空间预测,并与Logistic回归模型进行了比较。结果表明,两类支持向量机模型优于Logistic回归模型,而Logistic回归模型优于单类支持向量机模型。