Landslides 

About: Landslides is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Landslide & Geology. It has an ISSN identifier of 1612-510X. Over the lifetime, 2562 publications have been published receiving 76184 citations. The journal is also known as: Journal of the International Consortium on Landslides, ICL.

The Varnes classification of landslide types, an update

Abstract: The goal of this article is to revise several aspects of the well-known classification of landslides, developed by Varnes (1978). The primary recommendation is to modify the definition of landslide-forming materials, to provide compatibility with accepted geotechnical and geological terminology of rocks and soils. Other, less important modifications of the classification system are suggested, resulting from recent developments of the landslide science. The modified Varnes classification of landslides has 32 landslide types, each of which is backed by a formal definition. The definitions should facilitate backward compatibility of the system as well as possible translation to other languages. Complex landslides are not included as a separate category type, but composite types can be constructed by the user of the classification by combining two or more type names, if advantageous.

The rainfall intensity–duration control of shallow landslides and debris flows: an update

Abstract: A global database of 2,626 rainfall events that have resulted in shallow landslides and debris flows was compiled through a thorough literature search. The rainfall and landslide information was used to update the dependency of the minimum level of rainfall duration and intensity likely to result in shallow landslides and debris flows established by Nel Caine in 1980. The rainfall intensity–duration (ID) values were plotted in logarithmic coordinates, and it was established that with increased rainfall duration, the minimum average intensity likely to trigger shallow slope failures decreases linearly, in the range of durations from 10 min to 35 days. The minimum ID for the possible initiation of shallow landslides and debris flows was determined. The threshold curve was obtained from the rainfall data using an objective statistical technique. To cope with differences in the intensity and duration of rainfall likely to result in shallow slope failures in different climatic regions, the rainfall information was normalized to the mean annual precipitation and the rainy-day normal. Climate information was obtained from the global climate dataset compiled by the Climate Research Unit of the East Anglia University. The obtained global ID thresholds are significantly lower than the threshold proposed by Caine (Geogr Ann A 62:23–27, 1980), and lower than other global thresholds proposed in the literature. The new global ID thresholds can be used in a worldwide operational landslide warning system based on global precipitation measurements where local and regional thresholds are not available..

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

Abstract: Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.

Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models

Abstract: The aim of this study is to evaluate the landslide hazards at Selangor area, Malaysia, using Geographic Information System (GIS) and Remote Sensing. Landslide locations of the study area were identified from aerial photograph interpretation and field survey. Topographical maps, geological data, and satellite images were collected, processed, and constructed into a spatial database in a GIS platform. The factors chosen that influence landslide occurrence were: slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, land cover, vegetation index, and precipitation distribution. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors by frequency ratio and logistic regression models. The results of the analysis were verified using the landslide location data and compared with probability model. The comparison results showed that the frequency ratio model (accuracy is 93.04%) is better in prediction than logistic regression (accuracy is 90.34%) model.

Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan, China

Abstract: The 2008 Wenchuan earthquake (Ms = 8.0; epicenter located at 31.0° N, 103.4° E), with a focal depth of 19.0 km was triggered by the reactivation of the Longmenshan fault in Wenchuan County, Sichuan Province, China on 12 May 2008. This earthquake directly caused more than 15,000 geohazards in the form of landslides, rockfalls, and debris flows which resulted in about 20,000 deaths. It also caused more than 10,000 potential geohazard sites, especially for rockfalls, reflecting the susceptibility of high and steep slopes in mountainous areas affected by the earthquake. Landslide occurrence on mountain ridges and peaks indicated that seismic shaking was amplified by mountainous topography. Thirty-three of the high-risk landslide lakes with landslide dam heights greater than 10 m were classified into four levels: extremely high risk, high risk, medium risk, and low risk. The levels were created by comprehensively analyzing the capacity of landslide lakes, the height of landslide dams, and the composition and structure of materials that blocked rivers. In the epicenter area which was 300 km long and 10 km wide along the main seismic fault, there were lots of landslides triggered by the earthquake, and these landslides have a common characteristic of a discontinuous but flat sliding surface. The failure surfaces can be classified into the following three types based on their overall shape: concave, convex, and terraced. Field evidences illustrated that the vertical component of ground shaking had a significant effect on both building collapse and landslide generation. The ground motion records show that the vertical acceleration is greater than the horizontal, and the acceleration must be larger than 1.0 g in some parts along the main seismic fault. Two landslides are discussed as high speed and long runout cases. One is the Chengxi landslide in Beichuan County, and the other is the Donghekou landslide in Qingchuan County. In each case, the runout process and its impact on people and property were analyzed. The Chengxi landslide killed 1,600 people and destroyed numerous houses. The Donghekou landslide is a complex landslide–debris flow with a long runout. The debris flow scoured the bank of the Qingjiang River for a length of 2,400 m and subsequently formed a landslide dam. This landslide buried seven villages and killed more than 400 people.