Landslide Literature Research Articles

From spatio-temporal landslide susceptibility to landslide risk forecast

The literature on landslide susceptibility is rich with examples that span a wide range of topics. However, the component that pertains to the extension of the susceptibility framework toward space–time modeling is largely unexplored. This statement holds true, particularly in the context of landslide risk, where few scientific contributions investigate risk dynamics in space and time. This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years (from 2013 to 2021). For the analyses, the data cube is organized with a mapping unit consisting of 26,333 slope units repeated over an annual temporal unit, resulting in a total of 236,997 units. This phase already includes several interesting modeling experiments that have rarely appeared in the landslide literature (e.g., variable interaction plots). However, the main innovative effort is in the subsequent phase of the protocol we propose, as we used climate projections of the main trigger (rainfall) to obtain future estimates of yearly susceptibility patterns. These estimates are then combined with projections of urban settlements and associated populations to create a dynamic risk model, assuming vulnerability = 1. Overall, this manuscript presents a unique example of such a modeling routine and offers a potential standard for administrations to make informed decisions regarding future urban development.

High-Resolution Lidar-Derived DEM for Landslide Susceptibility Assessment Using AHP and Fuzzy Logic in Serdang, Malaysia

Landslide impact is potentially hazardous to an urban environment. Landslides occur at certain slope levels over time and require practical slope analysis to assess the nature of the slope where a landslide is likely to occur. Thus, acquiring very high-resolution remote sensing data plays a significant role in determining the slope surface. For this study, 12 landslide conditioning parameters with 10 × 10 cell sizes that have never been previously collectively applied were created. These factors were created directly from the LiDAR (Light Detection and Ranging) DEM (digital elevation model)using their layer toolboxes, which include slope, aspect, elevation, curvature, and hill shade. Stream power index (SPI), topographic wetness index (TWI), and terrain roughness index (TRI) were created from spatial layers such as slope, flow direction, and flow accumulation. Shapefiles of distances to roads, lakes, trees, and build-up were digitized as land use/cover from the LiDAR image and produced using the Euclidean distance method in ArcGIS. The parameters were selected based on expert knowledge, previous landslide literature, and the study area characteristics. Moreover, multicriteria decision-making analysis, which includes the analytic hierarchy process (AHP) and fuzzy logic approaches not previously utilized with a LiDAR DEM, was used in this study to predict the possibility of a landslide. The receiver operating characteristics (ROC) were used for the validation of results. The area under the curve (AUC) values obtained from the ROC method for the AHP and fuzzy were 0.859 and 0.802, respectively. The final susceptibility results will be helpful to urban developers in Malaysia and for sustainable landslide hazard mitigation.

On the prediction of landslide occurrences and sizes via Hierarchical Neural Networks

For more than three decades, the part of the geoscientific community studying landslides through data-driven models has focused on estimating where landslides may occur across a given landscape. This concept is widely known as landslide susceptibility. And, it has seen a vast improvement from old bivariate statistical techniques to modern deep learning routines. Despite all these advancements, no spatially-explicit data-driven model is currently capable of also predicting how large landslides may be once they trigger in a specific study area. In this work, we exploit a model architecture that has already found a number of applications in landslide susceptibility. Specifically, we opt for the use of Neural Networks. But, instead of focusing exclusively on where landslides may occur, we extend this paradigm to also spatially predict classes of landslide sizes. As a result, we keep the traditional binary classification paradigm but we make use of it to complement the susceptibility estimates with a crucial information for landslide hazard assessment. We will refer to this model as Hierarchical Neural Network (HNN) throughout the manuscript. To test this analytical protocol, we use the Nepalese area where the Gorkha earthquake induced tens of thousands of landslides on the 25th of April 2015. The results we obtain are quite promising. The component of our HNN that estimates the susceptibility outperforms a binomial Generalized Linear Model (GLM) baseline we used as benchmark. We did this for a GLM represents the most common classifier in the landslide literature. Most importantly, our HNN also suitably performed across the entire procedure. As a result, the landslide-area-class prediction returned not just a single susceptibility map, as per tradition. But, it also produced several informative maps on the expected landslide size classes. Our vision is for administrations to consult these suite of model outputs and maps to better assess the risk to local communities and infrastructure. And, to promote the diffusion of our HNN, we are sharing the data and codes in a githubsec repository in the hope that we would stimulate others to replicate similar analyses.

Robustness evaluation of fuzzy expert system and extreme learning machine for geographic information system-based landslide susceptibility zonation: A case study from Indian Himalaya

In the last few decades, with the development of computers and geographic information system (GIS), a wide range of landslide susceptibility zonation (LSZ) techniques were orchestrated by various researchers around the globe. Among them, the artificial intelligence (AI) have been distinctly regarded as the most effective and suitable approach to part with GIS for LSZ. Though, suitability of AI for LSZ is well addressed in the landslide literature, noises of processing data, choice of causative factors and landslide density of study area are the number of hindrances that cause quandary over preference of ideal AI technique among many. The current study intends to analyse and compare the predictive performance of two entirely different AI techniques, fuzzy expert system (FES), a bivariate statistical technique, and extreme learning machine (ELM), a multivariate statistical technique for GIS based LSZ. The Mussoorie Township, a famous tourist destination in the Indian State of Uttarakhand was taken as the study area. Thematic layers of relevant causative factors and landslide inventory were prepared for the study area through field survey, remote sensing, and GIS. The resultant landslide susceptibility maps (LSM) of the study area, LSM-I of FES and LSM-II of ELM were critically evaluated and compared with the aid of landslide inventory of the study area.

The 1963 Vajont Landslide: A Numerical Investigation on the Sliding Surface Heterogeneity

The 1963 Vajont landslide is a key case in landslide literature, because it was catastrophic and because a lot of accurate data were collected before and after its occurrence. In this paper, the main focus is on the possible heterogeneity of the sliding surface involved by the landslide motion, which is reflected by a heterogeneous distribution of the dynamic basal friction coefficient μ. Assuming a given zonation of the sliding surface, our strategy was to apply a 2D Lagrangian model to compute the landslide motion and to find the values of μ for each zone, leading to the best agreement between the computed and the observed final deposit. Following some hints from the literature, we have explored heterogeneous configurations composed of up to four different zones, including also the homogeneous case, by means of a 2D numerical model (UBO-BLOCK2) that handles the landslide as a mesh of blocks and runs quickly enough to allow the computation of tens of thousands of simulations in a reasonable computing time. It is found that the four-zone zonation produces the best fit (or the least misfit), which is a strong hint that the gliding surface involved different geotechnical units.

Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia

A comprehensive landslide susceptibility mapping (LSM) should be produced to reduce damages to individuals and infrastructures. In the international landslide literature, various statistical methods such as logistic regression (LR) and frequency ratio (FR) have been widely used individually for LSM. However, these methods have some weaknesses. This study aims to propose an ensemble method of FR and LR to overcome their weak points. This ensemble method was applied on Fayfa area which is located in the southwest of Saudi Arabia (SA) in Jazan region. The study area covers an area of ~260 km2, and is located between latitudes 17°11′46″N to 17°21′16′′N and longitudes 43°0′30′′E to 43°11′38′′E. For LSM, an inventory map with 86 landslide occurrences was extracted from various sources. Then, the landslide inventory was randomly divided into two datasets: 70 % for training the models and 30 % for validation. The landslide conditioning factors used in the LSL include altitude, curvature, distance from wadis, distance from road, distance from fault, stream power index, topographic wetness index, soil type, geology, slope, and aspect. The produced susceptibility maps were validated by an area under the curve technique. The success rate curves of the models measure the goodness of fit of the training data, while the prediction rate curves provide the validation of the susceptibility maps, measuring the prediction capability. The validation results showed 65, 69, and 79 % success rates for FR, LR, and ensemble models, respectively. Furthermore, the prediction rates were 58, 77, and 82 % for FR, LR, and ensemble models, respectively. The proposed ensemble method can be replicated in other natural hazard studies because it can produce accurate assessment for disaster management and decision making.

Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia

An 8 Richter Scale (RS) earthquake struck West Sumatra on Wednesday, 30 September 2009, at 17.16pm which led to huge number of landslides. Hence a comprehensive landslide susceptibility mapping (LSM) should be produced in order to reduce the damages to people and infrastructures. In the international landslide literature, various statistical methods such as frequency ratio (FR) and logistic regression (LR) have been widely used individually for LSM, but they have some weaknesses. FR which is able to perform bivariate statistical analysis (BSA) assesses the influence of classes of each conditioning factor on landslide occurrence. However, the correlation between the factors is mostly neglected. On the other hand, LR is able to analyze the relationship among the factors while it is not capable to evaluate the classes of each landslide conditioning factor. This paper aims to propose an ensemble method of FR and LR in order to overcome their weak points. For LSM, a landslide inventory map with a total of 87 landslide locations was extracted from various sources. Then the landslide inventory was randomly divided into two datasets 70% for training the models and the remaining 30% was used for validation purpose. The landslide conditioning factors consist of: altitude, curvature, river, SPI, rainfall, soil type, soil texture, land use/cover (LULC), peak ground acceleration (PGA), geology, slope, aspect, lineament and topographic wetness index (TWI). Four PGA of 7.5, 8, 8.6 and 9 were acquired and PGA 8 which was related to the 2009 earthquake was used to generate the model. Finally, the produced landslide susceptibility maps were validated using an area under the (ROC) curve method. For the model which was derived by PGA 8, the validation results showed 84% and 78% success and prediction rates respectively. Furthermore, the prediction rates for the models made by PGA 7.2, 8.6 and 9 are 79%, 78% and 81% respectively. The result proved the reasonable efficiency of the proposed method for earthquake induced landslide susceptibility mapping. Also the proposed ensemble method can be used in other hazard studies as it is capable to produce rapid and accurate assessment for disaster management and decision making.

Landslide Susceptibility Mapping in Yalova, Turkey, by Remote Sensing and GIS

Remote sensing and GIS (geographic information systems) techniques were used to map the landslide susceptibility in Yalova Province and to assess the landslide risk in its counties. The set of criteria investigated using GIS were determined as a result of a landslide literature survey. These criteria are geology, hydrology, terrain slope, vegetation cover, soil thickness, proximity to roads, soil moisture, and terrain aspect. Active/inactive landslide occurrences data were extracted from the 1:500,000 scale landslide maps prepared by the General Directorate of Mineral Research and Exploration of Turkey (MTA). Considering both the landslide occurrence statistics in the study area and the ranks given to the landslide-controlling criteria in previous landslide studies available in the literature, these criteria were evaluated in GIS, resulting in classification of a province into none, low, moderate, and high landslide susceptibility areas. A comparison of the resulting map with the original landslide map prepared by the MTA revealed that 91.1 percent of the locations corresponding to active creeps, slides, and flows 5 m deep. The resulting map showed new landslide risk areas as well, which were not on the original map of the MTA. These may be worthwhile to investigate while updating the original map for new potential landslide area research.

A statistical assessment on international landslide literature (1945–2008)

In the present study, the analysis of the international landslide literature is aimed. The landslide is perhaps one of the most complex natural phenomena. Also, due to landslides, a considerable amount of loss of lives and economic losses is encountered throughout the world. Although a vast amount of landslide papers is published in international journals, a statistical assessment on this literature is not encountered. For this reason, this study is performed. For the assessment of the international landslide literature, the Science Citation Index Expanded (WOS; Web of Science) published by Institute of Scientific Information (now Thomson Scientific), USA for the period 1945–2008, is considered. A total of 3,468 publications are found and this data is stored into Oracle XE database and queried by using Structured Query Language and Procedural Language/Structured Query Language. In the following stages, some statistical analyses are performed and the possible trends are discussed.