GIS-based bivariate statistical modelling for earthquake-triggered landslides susceptibility mapping related to the 2008 Wenchuan earthquake, China (original) (raw)
Related papers
2012
The main purpose of this study is to compare the following six GIS-based models for susceptibility mapping of earthquake triggered landslides: bivariate statistics (BS), logistic regression (LR), artificial neural networks (ANN), and three types of support vector machine (SVM) models that use the three different kernel functions linear, polynomial, and radial basis. The models are applied in a tributary watershed of the Fu River, a tributary of the Jialing River, which is part of the area of China affected by the May 12, 2008 Wenchuan earthquake. For this purpose, eleven thematic data layers are used: landslide inventory, slope angle, aspect, elevation, curvature, distance from drainages, topographic wetness index (TWI), distance from main roads, distance from surface rupture, peak ground acceleration (PGA), and lithology. The data layers were specifically constructed for analysis in this study. In the subsequent stage of the study, susceptibility maps were produced using the six models and the same input for each one. The validations of the resulting susceptibility maps were performed and compared by means of two values of area under curve (AUC) that represent the respective success rates and prediction rates. The AUC values obtained from all six results showed that the LR model provides the highest success rate (AUC ¼ 80.34) and the highest prediction rate (AUC ¼ 80.27). The SVM (radial basis function) model generates the second-highest success rate (AUC ¼80.302) and the second-highest prediction rate (AUC ¼ 80.151), which are close to the value from the LR model. The results using the SVM (linear) model show the lowest AUC values. The AUC values from the SVM (linear) model are only 72.52 (success rates) and 72.533 (prediction rates). Furthermore, the results also show that the radial basis function is the most appropriate kernel function of the three kernel functions applied using the SVM model for susceptibility mapping of earthquake triggered landslides in the study area. The paper also provides a counter-example for the widely held notion that validation performances of the results from application of the models obtained from soft computing techniques (such as ANN and SVM) are higher than those from applications of LR and BA models.
Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models
Water, 2019
Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, di...
Geosciences, 2019
The main purpose of this study is to comparatively assess the susceptibility of earthquake-triggered landslides in the island of Lefkada (Ionian Islands, Greece) using two different statistical analysis models, a bivariate model represented by frequency ratio (FR), and a multivariate model represented by logistic regression (LR). For the implementation of the models, the relationship between geo-environmental factors contributing to landslides and documented events related to the 17th November 2015 earthquake was investigated by geographic information systems (GIS)-based analysis. A landslide inventory with events attributed to the specific earthquake was prepared using satellite imagery interpretation and field surveys. Eight factors: Elevation, slope angle, slope aspect, distance to main road network, distance to faults, land cover, geology, and peak ground acceleration (PGA), were considered and used as thematic data layers. The prediction capability of the models and the accuracy of the resulting susceptibility maps were tested by a standard validation method, the receiver operator characteristic (ROC) analysis. Based on the validation results, the output map with the highest reliability could potentially constitute an ideal basis for use within regional spatial planning as well as for the organization of emergency actions by local authorities.
Comparative Study among Bivariate Statistical Models in Landslide Susceptibility Map
Indonesian Journal on Geoscience, 2020
The main purpose of this paper is to compare the performance of bivariate statistical models i.e. Frequency Ratio, Weight of Evidence, and Information Value for landslide susceptibility assessment. These models were applied in Cianjur Regency, West Java Province (Indonesia), in order to map the landslide susceptibility and to rate the importance of landslide causal factors. In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the Geographic Information System (GIS) supported by field investigations and remote sensing data. The 298 landslides were randomly divided into two groups of modeling/training data (70%) and validation/test data sets (30%). The landslide conditioning factors considered for the studied area were slope angle, elevation, slope aspect, lithological unit, and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weight of evidence (WofeE), and information value (IV). Model performance was tested with receiver operator characteristic analysis. The validation findings revealed that the three models showed promising results since the models gave good accuracy values. The success rates of FR, WofE, and IV models were 0.920, 0.926, and 0.930, while the prediction rates of the three models were 0.913, 0.912, and 0.895, respectively. However, the FR model was proved to be relatively superior in estimating landslide susceptibility throughout the studied area.
Statistical approach to earthquake-induced landslide susceptibility
Engineering Geology, 2008
Susceptibility analysis for predicting earthquake-induced landslides has most frequently been done using deterministic methods; multivariate statistical methods have not previously been applied. In this study, however, we introduce a statistical methodology that uses the intensity of earthquake shaking as a landslide triggering factor. This methodology is applied in a study of shallow earthquake-induced landslides in central western Taiwan. The results show that we can accurately interpret landslide distribution in the study area and predict the occurrence of landslides in neighboring regions. This susceptibility model is capable of predicting shallow landslides induced during an earthquake scenario with similar range of ground shaking, without requiring the use of geotechnical, groundwater or failure depth data.
Geomorphology, 2007
Statistical assessment of landslide susceptibility has become a major topic of research in the last decade. Most progress has been accomplished on producing susceptibility maps at meso-scales (1:50,000-1:25,000). At 1:10,000 scale, which is the scale of production of most regulatory landslide hazard and risk maps in Europe, few tests on the performance of these methods have been performed. This paper presents a procedure to identify the best variables for landslide susceptibility assessment through a bivariate technique (weights of evidence, WOE) and discusses the best way to minimize conditional independence (CI) between the predictive variables. Indeed, violating CI can severely bias the simulated maps by over-or under-estimating landslide probabilities. The proposed strategy includes four steps: (i) identification of the best response variable (RV) to represent landslide events, (ii) identification of the best combination of predictive variables (PVs) and neo-predictive variables (nPVs) to increase the performance of the statistical model, (iii) evaluation of the performance of the simulations by appropriate tests, and (iv) evaluation of the statistical model by expert judgment. The study site is the north-facing hillslope of the Barcelonnette Basin (France), affected by several types of landslides and characterized by a complex morphology. Results indicate that bivariate methods are powerful to assess landslide susceptibility at 1:10,000 scale. However, the method is limited from a geomorphological viewpoint when RVs and PVs are complex or poorly informative. It is demonstrated that expert knowledge has still to be introduced in statistical models to produce reliable landslide susceptibility maps.
Scientific Reports, 2021
Inventories of seismically induced landslides provide essential information about the extent and severity of ground effects after an earthquake. Rigorous assessment of the completeness of a landslide inventory and the quality of a landslide susceptibility map derived from the inventory is of paramount importance for disaster management applications. Methods and materials applied while preparing inventories influence their quality, but the criteria for generating an inventory are not standardized. This study considered five landslide inventories prepared by different authors after the 2015 Gorkha earthquake, to assess their differences, understand the implications of their use in producing landslide susceptibility maps in conjunction with standard landslide predisposing factors and logistic regression. We adopted three assessment criteria: (1) an error index to identify the mutual mismatches between the inventories; (2) statistical analysis, to study the inconsistency in predisposing factors and performance of susceptibility maps; and (3) geospatial analysis, to assess differences between the inventories and the corresponding susceptibility maps. Results show that substantial discrepancies exist among the mapped landslides. Although there is no distinct variation in the significance of landslide causative factors and the performance of susceptibility maps, a hot spot analysis and cluster/outlier analysis of the maps revealed notable differences in spatial patterns. The percentages of landslide-prone hot spots and clustered areas are directly proportional to the size of the landslide inventory. The proposed geospatial approaches provide a new perspective to the investigators for the quantitative analysis of earthquake-triggered landslide inventories and susceptibility maps.
ISPRS International Journal of Geo-Information
As a result of the Gorkha earthquake in 2015, about 9000 people lost their lives and many more were injured. Most of these losses were caused by earthquake-induced landslides. Sustainable planning and decision-making are required to reduce the losses caused by earthquakes and related hazards. The use of remote sensing and geographic information systems (GIS) for landslide susceptibility mapping can help planning authorities to prepare for and mitigate the consequences of future hazards. In this study, we developed landslide susceptibility maps using GIS-based statistical models at the regional level in central Nepal. Our study area included the districts affected by landslides after the Gorkha earthquake and its aftershocks. We used the 23,439 landslide locations obtained from high-resolution satellite imagery to evaluate the differences in landslide susceptibility using analytical hierarchy process (AHP), frequency ratio (FR) and hybrid spatial multi-criteria evaluation (SMCE) mode...
The evaluation and the sensitivity analysis of GIS-based landslide susceptibility models
Geosciences Journal, 2004
This paper evaluated two prevalent GIS-based models in landslide susceptibility; a deterministic-based approach and a bivariate-based approach. The bivariate-based approach defines the relationship between two variables individually, and this approach calculates the weights, which can also represent the contribution of factors. A deterministic-based approach is based on the geotechnical equation and calculates the quantitative values for landslide susceptibility. A number of GIS-based landslide projects and studies have been frequently conducted for identifying or estimating landslide prone areas using these two models. Results from those studies were easily implemented into disaster-related policies. Therefore, this paper highlighted discrepancies between these two models, and fulfilled the sensitivity analysis. After two models were run, the higher landslide susceptibility areas were quite different, and the reasons may come from the factors, the data availability, the data scale, and the data accuracy. The bivariate-based approach was sensitive to factors, and higher susceptible areas were new forest areas that have been presumably experienced by historical landslides. The deterministic-based approach was substantially affected by soil properties and slopes. The sensitivity analysis found that soil data should be complete and extensive for the accurate landslide susceptibility analysis.
SPRINGER-Arabian journal of Geosciences
Landslides are among the great destructive factors which cause lots of fatalities and financial losses all over the world every year. Studying of the factors affecting occurrence of landslides in a region and zoning the resulting damages will certainly play a crucial role in mitigating such phenomena. In this research, through geological maps and field studies, we primarily prepared a map for landslide distributions in Zab basin-an area of 520 km 2 in the southwest mountainsides of West Azerbaijan Province. By applying other source of information such as the existing thematic maps, we studied and defined the factors (slope, slope aspect, distance to road, distance to drainage network, distance to fault, land use and land cover, geological factors, horizontal gravity acceleration of earthquakes, and climatic condition of the studied area) that affect occurrence of the landslides. To get better precision and higher speed and facility in our analysis, all descriptive and spatial information were entered into geographic information system (GIS) system and Ilwis software. We also used Satellite images (Landsat ETM + and SPOT 5), producing land cover and landslide-inventory maps, respectively. After preparation of the influential parameters on landslides, we drew the zoning maps of slide hazard via four different statistical methods and then evaluated and compared them. By analyzing the obtained index and by comparing landslide distribution map and zoning map of landslide susceptibility prepared by each of the methods in GIS environment, we found that bivariate method of information value analysis, bivariate method of density-area, multivariate method with linear regression analysis, and multivariate method of discriminate analysis take priority, respectively. Finally, as this research shows, despite their simplicity, bivariate statistical methods have more acceptable precision than multivariate methods, and consequently, they are more compatible with landslide susceptibility of the region. From the results, lithology, slope, annual rainfall, land cover, slope aspect, distance to waterway, distance to road, horizontal gravity acceleration, and distance to fault are very influential to landslides in the region.