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Long-runout landslides can be extremely hazardous and unpredictable. The prediction of long-runout landslides is challenging because it is unclear what factors control their mobility, and it is unclear how to best measure their runout given that they traverse changing topography. In this paper, we document the development and statistical evaluation of a Landslide Runout Score (LRS) system to predict short, medium, and long runout using a new mobility measure, the unitless Runout Number L/A1/2 (where L is landslide length and A is landslide area). The Runout Number has previously been correlated to three geomorphological factors (planimetric curvature, sand content, and upslope contributing area normalized to landslide area), so the LRS uses these factors as inputs. These factors are readily calculated using geographic information systems (GIS) and publicly available data sources. The LRS system predicts runout categories (short, medium, and long) with one-vs.-all accuracies (i.e., percentage of correct predictions within a given class) of 75, 58, and 72%, respectively, for a total weighted accuracy of approximately 65%. The results of this work are summarized in a worksheet that can be used by geologists and engineers to develop preliminary predictions of landslide runout behavior, which can be incorporated into or used alongside regional-scale landslide hazard assessments. [ABSTRACT FROM AUTHOR]

Geomorphological analysis of landslide processes in mountainous terrains with difficult access has benefited from virtual representation of topography through the use of high-resolution digital elevation models (DEMs) generated by use of light detecting and ranging. Generic models of overlay and interpolation take advantage of the high-resolution DEMs for volume calculation. However, the advantages and limitations of the models at regional scale have received scant attention. These challenges are magnified in landslide hazard zonation mapping projects at state or national level, where the models need to be implemented for large datasets. To address the above deficiency, this paper presents a means to estimate landslide volume production and distribution by taking full advantage of LiDAR and to standardize landslide volume calculation in a geographic information system (GIS). We implemented two generic landslide volume models by using Python scripts; this is a systematic methodology for modeling volume of shallow and deep-seated landslides. The models were tested in real and theoretical conditions to highlight advantages and limitations. At the same time, we explored how the interpolation model is affected by local altimetric variation. The results show that one of the models can be used to make first-order interpretations regarding volume of eroded debris for landslide deposits at a local or national scale, while the other can help to assess the sequence of landslide activity. Theoretical evaluations show that local altimetric variation of < 1 m could lead to errors of almost 17%. The approach is explored with examples from Sumas Mountain in Whatcom County, Washington, USA. [ABSTRACT FROM AUTHOR]

The displacement of a large slow-moving landslide is accompanied by slope damage, such as fractures, tension cracks, and slope bulging. Studies of these features provide insight into the mechanisms responsible for the deformation. In this paper, we investigate slope damage at the Downie Slide, a very large landslide in British Columbia, Canada, that is slowly moving along two shear zones subparallel to the ground surface. Structural geology, and particularly the morphology of the lower shear zone, strongly controls the deformation and, in turn, the observed internal and surficial slope damage features. We use aerial and underground adit laser scanning and photogrammetry to characterize the geometry of the landslide. We subdivide the slide area into slope damage domains based on the distribution, size, and orientation of the slope damage features. We reconstruct the shape of the two failure surfaces by interpolating borehole inclinometer monitoring data and processing the 3D surfaces in a GIS environment to create aspect and slope maps. We observe a strong correlation between surficial slope damage features and changes in the dip and dip direction of the lower sliding surface. We further infer the presence of a multi-planar failure surface geometry and previously unrecognized structurally controlled damage zones. [ABSTRACT FROM AUTHOR]

This paper presents rainfall-induced landslide thresholds and predicts landslide hazard in Kuala Lumpur metropolitan city and surrounding areas. Landslide events from 2000 to 2012 were collected. The long and short antecedent rainfall days were prepared for landslide and non-landslide days simultaneously. First, threshold analysis was conducted by using data obtained from rainfall stations located in highly urbanized areas of Kuala Lumpur metropolis. Six rainfall gauges were selected, and the study area was divided into six zones according to rainfall gauges: Taman Desa Station (TD-KL), Genting Klang (GK-KL), LDG Edinburgh Station (LDGE-KL), SG Raya Hulu Langat Station (SRHL-Slg), Puchong Drop Station (PD-Slg), and Bukit Antarabangsa (BTA-Slg). After the threshold analysis was conducted for different periods (10, 15, and 30 days) in each station, reliability index test was conducted to optimize the best threshold that limits the predicted events along the study period for each region. Second, the threshold analysis results were used as input in the Poisson probability model to estimate landslide temporal probability ( P). Third, the spatial probability ( P) analysis was prepared by using the evidential belief function multiplied by the P to obtain the hazard maps for 1-, 3-, and 5-year scenarios. Finally, a validation process was conducted to test the prediction performance of the resultant hazard map for a 1- and 2-year prediction by using the landslide inventory of 2012 to early 2014, which was not included in the modeling of the hazard map. Results showed a valid correlation between the high and moderate hazardous areas for the six zones. The predicted hazard maps indicated a quantitative assessment of the prone areas and proved to be a valid disaster management tool. The produced hazard maps may play a vital role as input component in risk analysis. [ABSTRACT FROM AUTHOR]

An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. CHAID as a multivariate method has an automatic classification capacity to analyze large numbers of landslide conditioning factors. Moreover, it results two or more nodes for each independent variable, where every node contains numbers of presence or absence of landslides (dependent variable). Other DT methods such as Quick, Unbiased, Efficient Statistic Tree (QUEST) and Classification and Regression Trees (CRT) are not able to produce multi branches based tree. Thus, the main objective of this paper is to use CHAID method to perform the best classification fit for each conditioning factors, then, combined it with logistic regression (LR) to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. In the first step, a landslide inventory map with 296 landslide locations were extracted from various sources over the Pohang-Kyeong Joo catchment (South Korea). Then, the inventory was randomly split into two datasets, 70 % was used for training the models, and the remaining 30 % was used for validation purpose. Thirteen landslide conditioning factors were used for the susceptibility modeling. Then, CHAID was applied and revealed that some conditioning factors such as altitude, soil drain, soil texture and TWI, as terminal nodes and reflected the best classification fit. Then, a proposed ensemble technique was applied and the interpretations of the coefficients showed that the relationship between the decision tree branch nodes distance from drain, soil drain, and TWI, respectively, leads to better consequences assessment of landslides in the current study area. The validation results showed that both success and prediction rates, 75 and 79 %, respectively. This study proved the efficiency and reliability of ensemble DT and LR model in landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

It is well known that geological structure is one of the main predisposing factors that control landslide occurrence at regional scale. Along time, investigation of the role of structural and lithologic controls has raised the interest of specialists from various earth and planetary sciences who proposed different approaches that range from descriptive analysis to automatic detection. The aim of this study is to investigate the role of structural and lithological controls on landslide occurrence at regional scale. To achieve this, a collection of tools for calculating the TOBIA index and estimating the relationship between morphostructural and lithological conditions and landslide occurrences was developed using Python programming language. The relationship between geological structure and landslide occurrences was analyzed in three distinct landslide-prone sites in Romania, located in different geological and geomorphological contexts. The spatial distribution of the geological structure is modelled based on the TOBIA index (TOpographic/Bedding-plane Intersection Angle). The TOBIA index uses topographic slope, slope aspect, dip angle, and dip direction to divide the terrain into three categories named cataclinal, anaclinal, and orthoclinal, with each category having several subcategories. Additionally, by implementing the bivariate statistical analyses (Weight of Evidence), we investigated the relationship of landslide occurrences to the resulted morphostructural slopes, on the one hand, and to the lithological units, on the other hand. The output contrast values show an evident influence of the cataclinal slopes over landslide occurrences in homoclinal areas, with most of the scarps occurring at the contact between cataclinal and orthoclinal slopes. Fewer influences are found in areas where clay outcrops and where the geological structure has a much-diminished control over the presence of the landslides. These results contribute to a better understanding of the role of structural and lithologic controls on landslide occurrence at regional scale and furthermore could serve to a better assessment of landslide susceptibility. The results obtained on different geologic and geomorphic contexts in Romania suggest that this methodology can be applied successfully in other areas prone to landslides. For reproducibility purposes, the GIS set of tools was made available under the MIT license. [ABSTRACT FROM AUTHOR]

A complete landslide dam hazard management incorporates two assessment phases: the damming probability and the breach hazard. A prompt evaluation of the dam stability is crucial during the emergency to mitigate its consequences, but a reliable risk assessment can be realized only after the event has occurred, when the available time is very short. Therefore, it is necessary to develop tools able to help in mapping the spatial probability of damming over large areas for land-use planning, in order to better constrain consequence analysis and risk scenarios for setting up mitigation measures. In this work, a semi-automated GIS-based mapping methodology, based on a statistical correlation of morphometric parameters described by a morphological index, is proposed to spatially assess the likelihood of a river obstruction by landslide damming through two main mechanisms: the reactivation of existing landslides and the formation of new landslides. The two mapping methods (damming predisposition and damming probability) were used on a test area, the Arno River basin in Italy. The Eastern part of the basin resulted as the most susceptible to damming events in the whole basin. These are the highest mountain ridges in the basin (about 1600 m a.s.l.), characterized by calcareous, arenaceous, and marl lithology. The results are confirmed by the high concentration of the known historical landslide dams in the area according to existing inventories. [ABSTRACT FROM AUTHOR]

A landslide susceptibility map is a crucial tool for land-use spatial planning and management in mountainous areas. An essential issue in such maps is the determination of susceptibility thresholds. To this end, the map is zoned into a limited number of classes. Adopting one classification system or another will not only affect the map's readability and final appearance, but most importantly, it may affect the decision-making tasks required for effective land management. The present study compares and evaluates the reliability of some of the most commonly used classification methods, applied to a susceptibility map produced for the area of La Marina (Alicante, Spain). A new classification method based on ROC analysis is proposed, which extracts all the useful information from the initial dataset (terrain characteristics and landslide inventory) and includes, for the first time, the concept of misclassification costs. This process yields a more objective differentiation of susceptibility levels that relies less on the intrinsic structure of the terrain characteristics. The results reveal a considerable difference between the classification methods used to define the most susceptible zones (in over 20% of the surface) and highlight the need to establish a standard method for producing classified susceptibility maps. The method proposed in the study is particularly notable for its consistency, stability and homogeneity, and may mark the starting point for consensus on a generalisable classification method. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping is most effective if detailed surface and subsurface information can be combined with authoritative landslide catalogs or a deep understanding of local conditions. However, these types of homogeneous input data and catalogs are frequently not available over large areas. In this study, we model landslide susceptibility in Central America and the Caribbean islands by combining three globally available datasets and one regional dataset with fuzzy overlay. This primarily heuristic model provides the flexibility to test a range of different contributing variables and the capability to compare landslide inventories within the model framework that vary greatly in their size, spatiotemporal scope, and collection methods. We create a regional susceptibility map and evaluate its performance using receiver operating characteristics for both continuous and binned susceptibility values. This susceptibility map forms the basis for a near-real-time landslide hazard assessment system that couples susceptibility with rainfall and soil moisture triggers to estimate potential landslide activity at a regional scale. The application of this susceptibility model at the regional scale provides a foundation for transferring the methodology to other geographic areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]