Search

• Both models have applicability in prediction of group-occurring shallow landslides. • Combining Scoops3D and TRIGRS shows more effective landslide prediction result. • TRIGRS has the problem of over-prediction in the simulation of unstable area. • Fitting curve of rainfall threshold supports landslide warning in basin lacking data. The stability evaluation of rainfall-induced landslides using a physical determination model supports disaster prevention, but it is mostly applied to the area with few landslides, and there is a lack of quantitative study on rainfall and landslide stability. This paper combined the Scoops3D model with the TRIGRS model (3D) to predict the shallow landslide spatiotemporal distribution and compared the simulation results with those of the TRIGRS model alone (1D), aiming to obtain more accurate assessment results. At the same time, the relationship between landslide stability and accumulative rainfall was quantitatively fitted to improve the real-time landslide prediction system. We applied the 1D and 3D models to the July 21, 2013 group-occurring landslide event (976 shallow landslides) in the Niangniangba basin, China. The required geotechnical parameters of both models were acquired by field and laboratory tests. We calculated the pressure head over time using the TRIGRS model based on practical rainfall data and predicted the shallow landslide stability using the Scoops3D model according to the resulting piezometric surface. We compared the landslide stability spatial distributions of the two models under initial and saturated conditions with the landslide catalogue. The success rate of landslides predicted by 3D model is 4.20% higher than 1D model. A composite index to quantitatively evaluate both models' performances indicates over-prediction by the 1D model in the stable region, while the 3D model more effectively predicts shallow landslides with a smaller unstable region. The relationship between instability proportion and accumulative rainfall in the 1D and 3D model can be represented by y = 24.57 x 0.18 and y = 11.59 x 0.33 , respectively. The 3D model shows more conservative result, and the rainfall threshold analysis proposed in this paper can provide reference for the time of most landslides in the case of insufficient data, which is an important indicator for disaster early warning and prediction. [ABSTRACT FROM AUTHOR]

• The Random Naïve Bayes (RNB) was developed to evaluate the flood susceptibility. • A repeatedly random sampling method was adopted to select negative samples for RNB. • The RNB-based most accurate classifier can achieve better classification performance. • The framework can generate stable flood susceptibility maps. • RNB is a feasible approach for natural susceptibility assessment study. Random Naïve Bayes (RNB) is a machine learning method that uses the Random Forest (RF) structure to optimize Naïve Bayes (NB). It is interesting to see whether RNB could optimize NB and achieve satisfied assessment results like RF in the flood susceptibility assessment study. RNB has rarely been used in study of using machine learning methods to spatially analyze natural disasters, and thus it was selected as the analysis method. Based on the data feasibility, 12 spatial factors that affect the occurrence and spatial distribution of floods were selected. To avoid the influence of subjective equal-interval classification method, natural breaks and quantile method were used to discretize factors with continuous values, respectively. Here, a recently proposed repeatedly random sampling method was adopted to select negative samples for RNB to generate a most accurate classifier (MAC) that was employed to compute the probability of flood occurrence in the study area. Consequently, this paper adopted the integrated framework of GIS and RNB to spatially assess the flood susceptibility using the Wanan County in China as an instance. The results demonstrated that when integrated with the repeatedly random sampling method, the MAC-based flood susceptibility maps corresponding to different factor discretization methods were similar, meaning this framework can effectively avoid the effects caused by different factor discretization methods. Also, to testify the classification performance of RNB, RF and NB were chosen to compare the classification performance with it. The results indicated the classification performance in the order of RF > RNB > NB. This means RNB is able to achieve better classification performance than NB, but it exists limitations when compared with traditional strong classifiers like RF. The findings of this paper proved that RNB is a feasible approach for natural hazard susceptibility assessment. [ABSTRACT FROM AUTHOR]

Abstract: Vegetation is widely used for controlling shallow landslides. The mechanisms by which roots increase apparent soil cohesion is well documented and many values of root additional cohesion are available in the literature for different plant species. However, less information is given about the spatial variation of soil reinforcement by roots at a slope scale and its influence on slope stability, in particular in forest areas. The goal of this paper is to describe the spatial variability of root additional cohesion on two monospecific 17-y-old stands of Robinia pseudoacacia and Platycladus orientalis grown on slopes in the semiarid Loess Plateau of China, and to analyze numerically the effect of this variability on slopes stability. For this purpose, vertical trenches were dug at different distance and directions around trees situated at three different slope locations, i.e. up-, mid- and down-slope. Grids with a 10×10cm mesh were placed on vertical walls. Roots were counted within each grid cell and split according to their diameter class. Root area ratio (RAR) was estimated and compared among different positions around the trees and at three different locations along the slope. Roots tensile strength was determined with laboratory mechanical tests. RAR and root tensile strength were used as inputs in six different root reinforcement models to calculate root additional cohesion. A 2D finite element model of slope stability was developed and used to calculate the increase in factor of safety (FoS) due to root additional cohesion on rectilinear and terraced slopes. Results showed that both root tensile strength and Young''s modulus of R. pseudoacacia was about two times higher than tensile strength of P. orientalis. RAR distribution had a strong relationship with local soil moisture content measured in July during the raining season, and was significantly different with regards to tree location on the slope. The six theoretical models used to estimate the root additional cohesion gave different vertical profiles of root reinforcement distribution according to the underlying hypothesis on how forces are transferred to the roots. Theoretical analyses of slope stability showed that terraced slopes were 20% more stable than rectilinear slopes, disregarding the differences in hydrological regimes between these two terrain morphologies. Numerical sensitivity analyses also showed that the FoS reached an asymptotic value when increasing root additional cohesion. Actual additional cohesions of the two studied sites corresponded to FoS that were already close to this asymptotic values. Consequently variations of these actual root cohesions would not much affect slope stability. However it was showed that more attention should be given to the reinforcement of the bottom part of the actual slopes, where roots have a larger positive impact on the FoS. [Copyright &y& Elsevier]

• The induced factors of the Baige landslide are comprehensively investigated. • The Baige landslide is caused by deep-seated gravitational slope deformation. • River downcutting is a prerequisite for the evolution of the Baige landslide. • Earthquake contributes to accelerating the development of the Baige landslide. On October 11 and November 3, 2018, two successive landslides happened at the same location near Baige Village in Tibet of China and dammed the Jinsha River twice. The induced factors and formation mechanism of the Baige landslide are still being questioned. In this paper, high-resolution topographic data, multi-temporal remote sensing images (including the unmanned aerial vehicle (UAV) 3D image, remote sensing, and Geographical Information Systems (GIS) techniques), rainfall data, seismic records, comprehensive field investigations, and numerical simulations are used to identify the main characteristics, the induced factors, and the formation mechanism of the event. The results show that deep-seated gravitational slope deformations play an important role in the evolution process of the Baige landslide; river downcutting is a prerequisite for the progressive evolution of the gravitational slope deformation behavior of the Baige landslide, whereas earthquake plays a catalytic role in the development of the gravitational slope deformation of the Baige landslide. The Baige landslide would be a typical case study for understanding the formation mechanism of large-scale landslides along the Jinsha River. [ABSTRACT FROM AUTHOR]

The absence data (samples) for landslide susceptibility mapping using data-driven methods are not available directly and often approximated by locations where no landslides have occurred. The existing methods for generating absence data cannot quantify the reliability of candidate absence data and thus such data reduce the quality of prediction. In this paper, a new approach to absence data generation, referred to as similarity based sampling, was proposed for landslide susceptibility mapping using data-driven methods. First, the reliability of candidate absence data is quantified based on the dissimilarity in environmental conditions (covariate conditions) between the absence data and the presence data (which are the landslide occurrences). The absence data whose reliability value is higher than a given threshold were selected to be used. The proposed approach was validated through its application to three data-driven methods (i.e. logistic regression, support vector machine and random forest) for landslide susceptibility mapping. A case study was conducted in the Youfang catchment in southern Gansu Province of China. Ten groups of absence data were generated each corresponding to one of the ten different thresholds of reliability ranging from 0.0 to 0.9. The results show that the prediction accuracy of the data-driven methods rose when the threshold increased from 0.0 to 0.5, but the accuracy decreases as the threshold continues to increase after 0.5, that is, from 0.5 to 0.9. The best performance was obtained when the threshold was 0.5. The proposed method was compared with existing methods for absence data generation (i.e. buffer controlled and target space exteriorization). These results show that the similarity-based approach has a better performance than these existing methods for landslide susceptibility mapping using data-driven methods. • Similarity Based Sampling is proposed to generate absence data. • Landslide susceptibility mapping using statistic method • Prediction efficiency index was used to evaluate the accuracy of model. [ABSTRACT FROM AUTHOR]