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.
相似文献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.
相似文献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.
相似文献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 (rij) approaches were used. The susceptibility map produced by CGD1 has the highest AUC (0.817) and rij 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.
相似文献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.
相似文献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.
相似文献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.
相似文献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.
相似文献In this study, a new ensemble method was developed to assess landslide hazard models in Mt. Umyeon, South Korea, using the results of a physically based model as a conditioning factor (CF). Hydrological conditions were obtained from the national-scale rainfall threshold. To incorporate rainfall threshold in landslide initiation, national landslide inventory data were used to prepare I-D and C-D thresholds. A series of factor of safety (FS) distribution maps were prepared using a physically based model with a 12-h cumulative rainfall threshold. We created an ensemble model to overcome limitations in the physically based model, which could not incorporate important environmental variables such as hydrology, forest, soil, and geology. To determine the effect of CFs on landslide distribution, spatial data layers of elevation, drainage proximity, soil drainage characters, stream power index, sediment transport index, topographic wetness index, forest type, forest density, tree diameter, soil type geology, and the FS distribution map were analyzed in a maximum entropy-based machine learning algorithm. Validation was performed with a receiver operating characteristic curve (ROC). The ROC showed 65.9% accuracy in the physically based model, whereas the ensemble model had higher accuracy (79.6%) and a prediction rate of 89.7%. The ensemble landslide hazard model is a new approach, incorporating the FS distribution map into the available independent environmental variables.
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