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Preparation of landslide susceptibility map is the first step for landslide hazard mitigation and risk assessment. The main aim of this study is to explore potential applications of two new models such as two-class Kernel Logistic Regression (KLR) and Alternating Decision Tree (ADT) for landslide susceptibility mapping at the Yihuang area (China). The ADT has not been used in landslide susceptibility modeling and this paper attempts a novel application of this technique. For the purpose of comparison, a conventional method of Support Vector Machines (SVM) which has been widely used in the literature was included and their results were assessed. At first, a landslide inventory map with 187 landslide locations for the study area was constructed from various sources. Landslide locations were then spatially randomly split in a ratio of 70/30 for building landslide models and for the model validation. Then a spatial database with a total of fourteen landslide conditioning factors was prepared, including slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, landuse, normalized difference vegetation index (NDVI), lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Using the KLR, the SVM, and the ADT, three landslide susceptibility models were constructed using the training dataset. The three resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and five statistical evaluation measures. In addition, pairwise comparisons of the area under the ROC curve were carried out to assess if there are significant differences on the overall performance of the three models. The goodness-of-fits are 92.5% (the KLR model), 88.8% (the SVM model), and 95.7% (the ADT model). The prediction capabilities are 81.1%, 84.2%, and 93.3% for the KLR, the SVM, and the ADT models, respectively. The result shows that the ADT model yielded better overall performance and accurate results than the KLR and SVM models. The KLR model considered slightly better than SVM model in terms of the positive prediction values. The ADT and KLR are the two promising data mining techniques which might be considered to use in landslide susceptibility mapping. The results from this study may be useful for landuse planning and decision making in landslide prone areas. [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]