The Application of Artificial Neural Networks to Landslide Susceptibility Mapping at Janghung, Korea

The purpose of this study was to develop techniques for landslide susceptibility using artificial neural networks and then to apply these to the selected study area at Janghung in Korea. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use. Thirteen landslide-related factors were extracted from the spatial database. These factors were then used with an artificial neural network to analyze landslide susceptibility. Each factor's weight was determined by the back-propagation training method. Five different training sets were applied to analyze and verify the effect of training. Then the landslide susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. Landslide locations were used to verify results of the landslide susceptibility maps and to compare them. The artificial neural network proved to be an effective tool for analyzing landslide susceptibility.     

Validation of an artificial neural network model for landslide susceptibility mapping

The aim of this study was to validate an artificial neural network model at Youngin, Janghung, and Boeun, Korea, using the geographic information system (GIS). The factors that influence landslide occurrence, such as the slope, aspect, curvature, and geomorphology of topography, the type, material, drainage, and effective thickness of soil, the type, diameter, age, and density of forest, distance from lineament, and land cover were either calculated or extracted from the spatial database and Landsat TM satellite images. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the artificial neural network model. The landslide susceptibility analysis results were validated and cross-validated using the landslide locations as study areas. For this purpose, weights for each study area were calculated by the artificial neural network model. Among the nine cases, the best accuracy (81.36%) was obtained in the case of the Boeun-based Janghung weight, whereas the Janghung-based Youngin weight showed the worst accuracy (71.72%).     

Ensemble-based landslide susceptibility maps in Jinbu area, Korea

Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87.11% in frequency ratio, 83.14% in weight of evidence, 87.79% in logistic regression and 84.54% in artificial neural network) than the individual landslide susceptibility maps (84.94% in frequency ratio, 82.82% in weight of evidence, 87.72% in logistic regression and 81.44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model.     

Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea

The likelihood ratio, logistic regression, and artificial neural networks models are applied and verified for analysis of landslide susceptibility in Youngin, Korea, using the geographic information system. From a spatial database containing such data as landslide location, topography, soil, forest, geology, and land use, the 14 landslide-related factors were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by likelihood ratio, logistic regression, and artificial neural network models. Before the calculation, the study area was divided into two sides (west and east) of equal area, for verification of the models. Thus, the west side was used to assess the landslide susceptibility, and the east side was used to verify the derived susceptibility. The results of the landslide susceptibility analysis were verified using success and prediction rates. The verification results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.     

Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland,Malaysia

This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.     

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.     

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.     

Combining landslide susceptibility maps obtained from frequency ratio,logistic regression,and artificial neural network models using ASTER images and GIS

Landslide-related factors were extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, and integrated techniques were developed, applied, and verified for the analysis of landslide susceptibility in Boun, Korea, using a geographic information system (GIS). Digital elevation model (DEM), lineament, normalized difference vegetation index (NDVI), and land-cover factors were extracted from the ASTER images for analysis. Slope, aspect, and curvature were calculated from a DEM topographic database. Using the constructed spatial database, the relationships between the detected landslide locations and six related factors were identified and quantified using frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) models. These relationships were used as factor ratings in an overlay analysis to create landslide susceptibility indices and maps. Three landslide susceptibility maps were then combined and applied as new input factors in the FR, LR, and ANN models to make improved susceptibility maps. All of the susceptibility maps were verified by comparison with known landslide locations not used for training the models. The combined landslide susceptibility maps created using three landslide-related input factors showed improved accuracy (87.00% in FR, 88.21% in LR, and 86.51% in ANN models) compared to the individual landslide susceptibility maps (84.34% in FR, 85.40% in LR, and 74.29% in ANN models) generated using the six factors from the ASTER images.     

Statistical analysis of landslide susceptibility at Yongin, Korea

The aim of this study is to evaluate the susceptibility of landslides at Yongin, Korea, using a geographic information system (GIS). Landslide locations were identified in the Yongin area from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, timber cover, and geology. These data were collected and constructed into a spatial database using GIS. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, age, diameter, and density of timber were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM satellite image. Landslide susceptibility was analyzed using the landslide occurrence factors by probability and logistic regression methods. The results of the analysis were verified using the landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide location. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy. The results can be used to reduce associated hazards, and to plan land use and construction.     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.     

利用光学遥感数据、GIS及人工神经网络模型分析区域滑坡灾害

用光学遥感数据和地理信息系统(GIS)分析了马来西亚Selangor地区的滑坡灾害。通过遥感图像解译和野外调查,在研究区内确定出滑坡发生区。通过GIS和图像处理,建立了一个集地形、地质和遥感图像等多种信息的空间数据库。滑坡发生的因素主要为:地形坡度、地形方位、地形曲率及与排水设备距离;岩性及与线性构造距离;TM图像解译得到的植被覆盖情况;Landsat图像解译得到的植被指数;降水量。通过建立人工神经网络模型对这些因素进行分析后得到滑坡灾害图:由反向传播训练方法确定每个因素的权重值,然后用该权重值计算出滑坡灾害指数,最后用GIS工具生成滑坡灾害图。用遥感解译和野外观测确定出的滑坡位置资料验证了滑坡灾害图,准确率为82.92%。结果表明推测的滑坡灾害图与滑坡实际发生区域足够吻合。     

Landslide detection and susceptibility mapping in the Sagimakri area, Korea using KOMPSAT-1 and weight of evidence technique

The purpose of this study is to detect landslide locations from satellite images and use them for landslide susceptibility mapping in the Sagimakri area, Korea using a geographic information system and a data-driven weight of evidence model. The landslide location areas were identified from Korea multipurpose satellite images by means of change detection technique and further verified by extensive field survey. Subsequently, landslide locations were randomly selected in a 70:30 ratio for training and validation of the model, respectively. A spatial database was constructed, which is composed of topography, forest, soil, and land cover, and 14 landslide-related factors were extracted from the database. The relationships between the detected landslide locations and the factors were identified and quantified by weights of evidence model. Tests of conditional independence were performed for the selection of factors, allowing five different combinations of factors to be analyzed. The relationships were used as the contrast values, W ⁺ and W ^? of factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. The results of the analysis were validated by comparison with known landslide locations that were not used directly in the analysis.     

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%).     

Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.     

基于GIS与ANN模型的地震滑坡易发性区划

基于遥感数据、地理信息系统(GIS)技术和人工神经网络(ANN)模型,开展地震滑坡易发性区划研究.2010年4月14日玉树地震后,基于航片与卫星影像目视解译,并辅以野外调查的方法,在地震区圈定了2036处地震诱发滑坡.选择高程、坡度、坡向、斜坡曲率、坡位、与水系距离、地层岩性、与断裂距离、与公路距离、归一化植被指数(NDVI)、与同震地表破裂距离、地震动峰值加速度(PGA)共12个因子作为地震滑坡易发性评价因子.这些因子均是应用GIS技术与遥感影像处理技术,基于地形数据、地质数据、遥感数据得到.训练样本中的滑动样本有两组,一组是滑坡区整个单滑坡体的质心位置,另一组是滑坡滑源区滑前的坡体高程最高的位置.应用这12个影响因子,分别采用这两组评价样本,基于ANN模型建立地震滑坡易发性索引图,基于GIS工具建立地震滑坡易发性分级图.分别应用训练样本中滑坡分布的点数据去检验各自的结果正确率,正确率分别为81.53％与81.29％,表明ANN模型是一种高效科学的地震滑坡易发性区划模型.     

Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models

This study applied, tested and compared a probability model, a frequency ratio and statistical model, a logistic regression to Damre Romel area, Cambodia, using a geographic information system. For landslide susceptibility mapping, landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and a spatial database was constructed from topographic maps, geology and land cover. The factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from lineament were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite imagery. The relationship between the factors and the landslides was calculated using frequency ratio and logistic regression models. The relationships, frequency ratio and logistic regression coefficient were overlaid to make landslide susceptibility map. Then the landslide susceptibility map was compared with known landslide locations and tested. As the result, the frequency ratio model (86.97%) and the logistic regression (86.37%) had high and similar prediction accuracy. The landslide susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.     

Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand

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

Landslide susceptibility zonation in Greece

The objective of this study is to perform a preliminary national-scale assessment of the landslide susceptibility in Greece using a landslide inventory derived from historical archives. The effects of controlling factors on landslide susceptibility combined with multivariate statistics have been evaluated using GIS aided mapping techniques. Thousand six hundred thirty-five landslide occurrences, mainly earth slides obtained from Public Authorities archives, covering a long time period were recorded and digitally stored using a spatial relational database management system. Ten landslide predisposing factors (predictors) were identified, while digital thematic maps on the spatial distribution of those factors were generated. The correlation between the landslide locations and predictor classes was analyzed by using the Landslide Relative Frequency. R-mode factor analysis was applied to study the interrelations between predictors (independent variables) while weighting coefficients were determined. Landslide susceptibility was derived from an algorithm which modeled the influence of predictors, and a susceptibility map was compiled. The landslide susceptibility map was verified using a data set of 375 new landslide locations. It is the first comprehensive attempt to illustrate the landslide susceptibility in the total country based on the interpretation of historical data only.     

Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway)

Statistical models are one of the most preferred methods among many landslide susceptibility assessment methods. As landslide occurrences and influencing factors have spatial variations, global models like neural network or logistic regression (LR) ignore spatial dependence or autocorrelation characteristics of data between the observations in susceptibility assessment. However, to assess the probability of landslide within a specified period of time and within a given area, it is important to understand the spatial correlation between landslide occurrences and influencing factors. By including these relations, the predictive ability of the developed model increases. In this respect, spatial regression (SR) and geographically weighted regression (GWR) techniques, which consider spatial variability in the parameters, are proposed in this study for landslide hazard assessment to provide better realistic representations of landslide susceptibility. The proposed model was implemented to a case study area from More and Romsdal region of Norway. Topographic (morphometric) parameters (slope angle, slope aspect, curvature, plan, and profile curvatures), geological parameters (geological formations, tectonic uplift, and lineaments), land cover parameter (vegetation coverage), and triggering factor (precipitation) were considered as landslide influencing factors. These influencing factors together with past rock avalanche inventory in the study region were considered to obtain landslide susceptibility maps by using SR and LR models. The comparisons of susceptibility maps obtained from SR and LR show that SR models have higher predictive performance. In addition, the performances of SR and LR models at the local scale were investigated by finding the differences between GWR and SR and GWR and LR maps. These maps which can be named as comparison maps help to understand how the models estimate the coefficients at local scale. In this way, the regions where SR and LR models over or under estimate the landslide hazard potential were identified.     

Cross-application used to validate landslide susceptibility maps using a probabilistic model from Korea

A study of landslides in Youngin, Janghung and Boeun, Korea, using the geographic information system (GIS) validates a spatial probabilistic model for landslide susceptibility analysis. Locations were identified from aerial photographs, satellite images and field surveys. Topography, soil-type, forest-cover and land-cover maps were constructed from spatial data sets. Landslide occurrence is influenced by 13 factors, evidence for which was extracted from the database with the frequency ratio of each factor computed. Landslide susceptibility maps use frequency ratios derived not only from data for each area but also ratios, one from the probabilistic model, calculated from the other two areas (nine maps in all) as a cross-check of method validity. For validation, analytical results were compared in each study area with actual landslide locations: Boeun based on its frequency ratio showed the best accuracy (82.49%) whereas Janghung based on the Boeun frequency ratio showed the worst (69.53%).