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[Display omitted] • Hoeffding Tree is a promising landslide susceptibility model based on ROC and AUC. • Bayes Network and Logistic Model Tree were applied for comparison. • Correlation analysis of conditioning factors was completed by Frequency Ratio. • Landslide susceptibility maps of Muchuan County and the county town were produced. Landslides, one of the most common hazards around the world, have brought about severe damage to life and property of human. To prevent and mitigate landslides, various models have been introduced to assess landslide susceptibility. In this paper, Hoeffding Tree (HT), a prevailing data stream mining algorithm, was employed to predict landslide susceptibility in Muchuan County, China for the first time. Meanwhile, Logistic Model Tree (LMT) and Bayes Network (BN) were applied to produce landslide susceptibility maps for comparison. The model performances were evaluated by Receiver Operating Characteristic (ROC) curves and areas under the curves (AUC). To obtain landslide inventory map, 279 landslides data was collected, and training and validation datasets were randomly divided with a proportion of 70% to 30%. Furthermore, twelve conditioning factors (altitude, slope angle, profile curvature, plan curvature, slope aspect, distance to roads, distance to rivers, TWI, NDVI, soil, land use and lithology) were selected to construct landslide susceptibility models. Moreover, correlations between conditioning factors and landslides were analyzed using Frequency Ratio (FR). The results showed landslides are prone to occur in areas where human activities concentrate, and all three models exhibited satisfying performances. Concretely, for training dataset, LMT model showed the highest AUC (0.854), followed by HT (0.726) and BN (0.709). However, for validation dataset, LMT and BN models generated similar AUC values (0.761 and 0.764 respectively), and the highest AUC value belonged to HT (0.802). The distributions of landslide susceptibility zones revealed that the interior of county town is mainly seated in low and very low susceptibility zones, whereas regions close to the border suffer high and very high landslide risk. The results acquired in this paper are significant to landslide prevention and urban planning in Muchuan, China. Additionally, this study proved that HT model is a promising classifier for landslide susceptibility modeling. [ABSTRACT FROM AUTHOR]

• 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]

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]

• 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]