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Complete landslide inventories are rarely available. The objectives of this study were to (i) elaborate the influence of incomplete landslide inventories on statistical landslide susceptibility models and to (ii) propose suitable modelling strategies that can reduce the effects of inventory-based incompleteness. In this context, we examined whether the application of a novel statistical approach, namely mixed-effects models, enables predictions that are less influenced by such inventory-based errors. The study was conducted for (i) an area located in eastern Austria and (ii) a synthetically generated data set. The applied methodology consisted of a simulation of two different inventory-based biases and an in-depth evaluation of subsequent modelling results. Inventory-based errors were simulated by gradually removing landslide data within forests and selected municipalities. The resulting differently biased inventories were introduced into logistic regression models while we considered the effects of including or excluding predictors that are directly related to the respective inventory-based bias. Mixed-effects logistic regression was used to account for variation that was due to an inventory-based incompleteness. The results show that most erroneous predictions, but highest predictive performances, were obtained from models generated with highly incomplete inventories and predictors that were able to directly describe the respective incompleteness. An exclusion of such bias-describing predictors led to systematically confounded relationships. The application of mixed-effects models proved valuable to produce predictions that were least affected by inventory-based errors. This paper highlights that the degree of inventory-based incompleteness is only one of several aspects that determine how an inventory-based bias may propagate into the final results. We propose a four-step procedure to deal with incomplete inventories in the context of statistical landslide susceptibility modelling. [ABSTRACT FROM AUTHOR]

This paper presents a new region-based preparatory factor, total flux ( TF), for landslide susceptibility models (LSMs). TF takes into account the topography and hydrology conditions upstream of each gridded data cell and represents the total flux of water in the stream. The results show that TF is strongly associated with the occurrence of landslides and is a good preparatory factor for LSM. Using TF instead of a drainage distance factor in I-Lan region in Taiwan shows an improvement in the accuracy of the cumulative percentage of landslide occurrence of 44 and 14 % for the top 1 and 10 % susceptible areas, respectively. This significant improvement in accuracy in these high-risk areas is critical for preventing and mitigating the economic and human losses due to landslides. [ABSTRACT FROM AUTHOR]

Fifty-five descending images from the ENVISAT satellite were processed using the small baseline subset (SBAS) method to derive the spatial and temporal ground deformation of the Bailong River Basin between 2003 and 2010. The basin is one of the most severely landslide- and debris flow-affected areas of China. As a result, 104 sites with high deformation areas were identified. Interferometric Synthetic Aperture Radar (InSAR) analysis was combined with landslide inventory data and field surveys, and anomalous areas were classified into three main types: landslide; debris; and subsidence. Displacement rates up to 35 mm/yr were evaluated away from the sensor along a line-of-sight (LOS) direction. The results gained should allow a more accurate prediction and monitoring of landslides, debris, and subsidence; further, they demonstrate the capability of the SBAS method to analyze any displacement effect and identify dangerous and uninhabitable areas in the basin. The small baseline subset method can thus contribute to the prediction and prevention of geohazards in the area. [ABSTRACT FROM AUTHOR]

This work focuses on the development of a combined statistical-mechanical approach to predict changes in landslide displacement rates from observed changes in rainfall amounts. The forecasting tool FLAME (Forecasting Landslides Accelerations induced by Meteorological Events) associates (1) a statistical impulse response (IR) model to simulate the changes in landslide rates by computing a transfer function between the input signal (e.g. rainfall) and the output signal (e.g. displacement) and (2) a simple 1D mechanical (MA) model (e.g. viscoplastic rheology) to take into account changes in pore water pressure. The models have been applied to forecast the displacement rates at the Super-Sauze landslide (South East France). The performance of different combinations of models (IR model alone, MA model alone and a combination of the IR and MA models) is evaluated against observed changes in pore water pressures and displacement rates at the study site. Results indicate that the three models are able to reproduce the displacement pattern in the general kinematic regime (succession of acceleration and deceleration phases); conversely, extreme kinematic regimes such as fluidization of part of the landslide mass are not being reproduced. The approach constitutes however a robust tool to predict changes in displacement rates from rainfall or groundwater time series. [ABSTRACT FROM AUTHOR]

Logistic regression studies which assess landslide susceptibility are widely available in the literature. However, a global review of these studies to synthesise and compare the results does not exist. There are currently no guidelines for the selection of covariates to be used in logistic regression analysis, and as such, the covariates selected vary widely between studies. An inventory of significant covariates associated with landsliding produced from the full set of such studies globally would be a useful aid to the selection of covariates in future logistic regression studies. Thus, studies using logistic regression for landslide susceptibility estimation published in the literature were collated, and a database was created of the significant factors affecting the generation of landslides. The database records the paper the data were taken from, the year of publication, the approximate longitude and latitude of the study area, the trigger method (where appropriate) and the most dominant type of landslides occurring in the study area. The significant and non-significant (at the 95 % confidence level) covariates were recorded, as well as their coefficient, statistical significance and unit of measurement. The most common statistically significant covariate used in landslide logistic regression was slope, followed by aspect. The significant covariates related to landsliding varied for earthquake-induced landslides compared to rainfall-induced landslides, and between landslide type. More importantly, the full range of covariates used was identified along with their frequencies of inclusion. The analysis showed that there needs to be more clarity and consistency in the methodology for selecting covariates for logistic regression analysis and in the metrics included when presenting the results. Several recommendations for future studies were given. [ABSTRACT FROM AUTHOR]

A debris-flow hazard assessment was conducted for the Medano Creek drainage basin, at Great Sand Dunes National Park and Preserve, following the 2,400 ha Medano Fire in 2010. Debris-flow probability and volume predictions were made using empirical regression models. Model parameters include burn severity, rainfall intensity, topographic characteristics, and soil properties. Model results provided park resource managers with information on potential sub-basin-specific hazards to park visitors, roads, and campsites. Monitoring of the first significant rainfall events following the fire and the resulting debris-flow responses provided means for model validation. Of the three probability models utilized, two predicted high probability of debris-flow occurrence for all sub-basins that produced debris flows, with numerous false positives, while the third failed to predict high probability in any of the sub-basins. The volume model predicted volumes within approximately one order of magnitude higher than those measured. [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]

The prediction of landslide movement acceleration is a complex problem, among others identified for deep-seated landslides, and represents a crucial step for risk assessment. Within the scope of this problem, the objective of this paper is to explore a modelling method that enables the study of landslide function and facilitates displacement predictions based on a limited data set. An inverse modelling approach is proposed for predicting the temporal evolution of landslide movement based on rainfall and displacement velocities. Initially, the hydrogeology of the studied landslides was conceptualised based on correlative analyses. Subsequently, we applied an inverse model with a Gaussian-exponential transfer function to reproduce the displacements. This method was tested on the Grand Ilet (GI) and Mare-à-Poule-d'Eau (HB) landslides on Reunion Island in the Indian Ocean. We show that the behaviour of landslides can be modelled by inverse models with a bimodal transfer function using a Gaussian-exponential impulse response. The cumulative displacements over 7 years of modelling (2 years of calibration period for GI, and 4 years for HB) were reproduced with an RMSE above 0.9. The characteristics of the bimodal transfer function are directly related to the hydrogeological functioning demonstrated by the correlative analyses: the rapid reaction of a landslide can be associated with the effect of a preferential flow path on groundwater level variations. Thus, this study shows that the inverse model using a Gaussian-exponential transfer function is a powerful tool for predicting deep-seated landslide movements and for studying how they function. Beyond modelling displacements, our approach effectively demonstrates its ability to contribute relevant data for conceptualising the sliding mechanisms and hydrogeology of landslides. [ABSTRACT FROM AUTHOR]