Statistical approach to earthquake-induced landslide susceptibility (original) (raw)
Related papers
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.
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.
Common Patterns Among Different Landslide Susceptibility Models of the Same Region
Advancing Culture of Living with Landslides, 2017
Four rain-event landslide inventories and one combined-event dataset for the mountainous terrain around the Choswei river catchment area in central Taiwan were selected for studies. A total of five event-based landslide susceptibility analyses were completed, and one multi-temporal landslide inventory was used to carry out regular landslide susceptibility analysis. The basic susceptibility of each model was compared and a common pattern of susceptibility was found among them. The results indicate that there is a common pattern of landslide susceptibility in a given region regardless of which event is used to build the susceptibility model. Also, the basic susceptibility is similar in pattern to the susceptibility model built based upon the multi-temporal landslide inventory of that region.
High-intensity earthquakes are capable of simultaneously triggering multiple landslides that cover a broad area. These earthquaketriggered landslides are significant contributors to loss of life and economic devastation in earthquake-affected regions throughout the world Xu et al. 2012a,f). To develop effective landslide disaster management and hazard mitigation strategies in earthquake-prone environments, it is very important to establish comprehensive inventories of landslides triggered by earthquake events and to use these inventories to generate seismic landslide susceptibility analyses (e.g. Lin
Water
This study analyzed the potential of landslides induced by the interaction between rainfall and earthquakes. Dapu Township and Alishan Township in Chiayi County, southern Taiwan, were included as study areas. From satellite images and the literature, we collected data for multiple years and time series and then used the random forest data mining algorithm for satellite image interpretation. A hazard index for the interaction between earthquakes and rainfall (IHERI) was proposed, and an index for the degree of land disturbance (IDLD) was estimated to explore the characteristics of IHERI under specific natural environmental and slope land use conditions. The results revealed that among the investigated disaster-causing factors, the degree of slope land use disturbance, the slope of the natural environment, and rainfall exerted the strongest effect on landslide occurrence. When IHERI or IDLD was higher, the probability of a landslide also increased, and under conditions of a similar ID...
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.
Assessment of earthquake-triggered landslide susceptibility considering coseismic ground deformation
Frontiers in Earth Science, 2023
The distance to the surface rupture zone has been commonly regarded as an important influencing factor in the evaluation of earthquake-triggered landslide susceptibility. However, the obvious surface rupture zones usually do not occur in some buried-fault earthquake cases, which means information about the distance to the surface rupture is lacking. In this study, a new influencing factor named coseismic ground deformation was added to remedy this shortcoming. The Mid-Niigata prefecture earthquake was regarded as the study case. To select a more suitable model for generating the landslide susceptibility map, three commonly used models named logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM) were also conducted to assess landslide susceptibility. The performances of these three models were evaluated with the receiver operating characteristic curve. The calculated results showed that the ANN model has the highest area under the curve (AUC) value of 0.82. As the earthquake triggered more landslides in the epicenter area, which makes it more prone to landslides in further earthquakes, the susceptibility analysis at two different mapping scales (the whole study area and the epicenter area) was also applied.
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.
Landslides, 2006
The Suusamyr region is located in the northern part of the Tien Shan Range in Central Asia. In 1992, this region was hit by the Ms = 7.3 Suusamyr earthquake triggering several large landslides along the Suusamyr Valley and on the southern slopes of the adjacent Suusamyr Range. One of these landslides had been investigated by geophysical and geotechnical methods in order to determine local trigger factors. The present paper focuses on the influence of geological and morphological factors upon landslide occurrence on a regional scale. The analysis is based on a digital data set including landslides triggered in 1992 and several older landslides as well as various types of digital elevation models (DEMs), ASTER image data, and geological and active fault maps. These data were combined to compute landslide susceptibility (LS) maps using statistical methods, Landslide Factor and Conditional Analyses (LFA, CA), as well as a geotechnical one, the Newmark's Method (NM). The landslide data set was also analyzed with respect to the size–frequency relationship.
gisc.gr
Landslides are considered among the most important geohazards as they cause substantial losses worldwide. Landslide susceptibility assessment (LSA) is related to certain influencing factors. Statistical modelling is one of the most preferred methods for LSA and mapping. Although landslide occurrences and influencing factors have spatial variations, global models ignore spatial dependence or autocorrelation characteristics of data in susceptibility assessment. In this paper the use of global and local LSA methods is examined. Thus, geographical weighted regression (GWR) is compared to standard logistic regression (LR) approach. The proposed models were implemented to Peloponnese peninsula in south Greece. Topographic (morphometric) parameters (slope angle, elevation), geological parameters and other parameters (landcover, rainfalls), were considered as landslide influencing factors. These influencing factors as well as the landslide events inventory in the study area were considered in order to obtain landslide susceptibility maps by using LR and GWR models. The results show the potential improvement in landslide susceptibility assessment by the use of GWR.