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A large-scale obliquely inclined bedding rockslide, activated by a heavy rainstorm, occurred on July 8, 2020, at 7:05 (UTC + 8) in Shiban Village, Songtao Miao Autonomous County, Guizhou Province, China. The loss of life in this event was greatly reduced owing to the local warning system for rainstorm-induced geohazards. To understand the failure characteristics, triggering factors, the genetic mechanism of the landslide, the geomorphological features, geological characteristics, hydrological conditions, and rainfall characteristics were systematically studied by a synthetic approach including field investigations, satellite imagery, unmanned aerial vehicle (UAV) photography, laboratory tests, and rainfall data statistics. The results indicated that the interface between the soft and hard rock, the well-developed joints, and the free face in front of the slope constituted the boundaries of this landslide. The concave topography at the back and southern edge of the landslide, the bare ground, and the cataclastic structure of the rock mass provided favorable conditions for the collection or infiltration of rainwater. The concentrated rainstorm was the direct trigger for the landslide, which led to a rapid inflow and retention of rainfall in the landslide through favorable landform and geological conditions. The groundwater recharge that cannot be drained in time caused the mechanical deterioration of rock mass and induced a rapid increase in pore water pressure in the landslide. Moreover, the water level of the Ganlong River at the toe of the slope also rose rapidly, and the uplift pressure in front of the slope increased accordingly. Under the combined action of these adverse factors, the overall anti-sliding force of the slope was less than the sliding force, finally resulting in the landslide. Remarkably, the local warning system for rainstorm-induced geohazards successfully forecasted the landslide, but the shortcoming is that the forecast time in advance is short. Nevertheless, the prediction has significantly reduced human casualties and provided valuable experience for the prediction of this type of landslide. [ABSTRACT FROM AUTHOR]

On 22 October 2018, at 17:20 (UTC+8), a bedding rockslide occurred on the right bank of the Jian-En expressway in Jianshi County, Hubei Province, Central China. Fortunately, a systematic displacement monitoring network provided an early warning. Thus, no casualties were caused by the rockslide, although two transport vehicles and three houses were damaged. To deduce the triggering factors and failure mechanism of the bedding rockslide, the geological characteristics, hydrogeological characteristics, slope excavation scheme, and rainfall data statistics were systematically investigated based upon field investigations, UAV photography, geological drilling, and laboratory tests. It was found that the bedding characteristics, presence of well-developed joints, and properties of the weak interlayers were the internal factors inducing the rockslide. The free face in front of the slope, the weak interlayer with low shear strength, and the through-going joint that formed the back boundary of slope constituted the boundary of the rockslide. The combined effects of improper slope excavation and concentrated rainfall were external factors that directly triggered the rockslide. The well-developed joints provided an effective infiltration channel for rainwater, and continuous rainfall deteriorated the shear strength of the weak interlayer ultimately causing the rockslide. [ABSTRACT FROM AUTHOR]

Monitoring has been considered to be the most effective means of preventing and mitigating geo-hazards. Unmanned aerial vehicle (UAV) based remote sensing has been proven to be a viable method for monitoring relatively flat areas, but has typically encountered challenges on steep mountainous slopes. The Qinglingou Slope, which is located at the head of the Three Gorges Reservoir, China, was selected as a case study, and a UAV-based remote sensing method was applied to monitor this steep mountainous slope. The UAV, which was equipped with a camera, was used to acquire images on May 10 and Sept. 13, 2015. At the same time, a plan for establishing and measuring a detailed network of ground control points (GCPs) and check points was carefully designed and implemented, according to the local conditions. Georeferenced digital surface models (DSMs) and digital orthophotos were obtained by photogrammetric processing. Based on the results, a simple and quick method that identified and quantified slope deformation by calculating and analyzing the changes in the DSMs was developed. The results showed that the UAV-based remote sensing method was rapid, safe and effective, and it could be valuable for monitoring a large number of steep mountainous slopes in the Three Gorges Reservoir. [ABSTRACT FROM AUTHOR]

Rainfall reservoir-induced landslides in the Zigui Basin, China Three Gorges Reservoir (CTGR) area, exhibit typical step-like deformation characteristics with mutation and creep states. Previous landslide displacement forecasting models yielded low prediction accuracy especially for mutational displacements. Coupled with the lack of monitoring sites and data limitations, it is extremely difficult to obtain accurate and reliable early warnings for landslides. The multi-feature fusion transfer learning (MFTL) method proposed in this paper applies the knowledge and skills obtained from the Baijiabao landslide scenario and sufficient monitoring data to improve the prediction capacity for other landslides, such as the Bazimen and Baishuihe landslides. The model barely relies on the long-time continuous monitoring process, and it can not only fill gaps in data when monitoring is interrupted, but also provide real-time displacement predictions based on accurate weather forecasting and periodic reservoir scheduling. In addition, the non-uniform weight error (NWE) evaluation method is proposed in this paper to focus more on the mutation state prediction accuracy because landslide instability is most likely to occur in this stage. Compared with other intelligent algorithms, the results indicate that the MFTL method owns low prediction error and high reliability, as well as the positive generalization ability in landslide prediction. This study paves the potential way for realizing the real-time, whole-process and accurate landslide forecasting. • An innovative model based on Transfer Learning method is firstly applied in landslide displacement prediction. • The model barely relies on long-time continuous monitoring dataset and it can fill data gaps due to monitoring interruptions. • The model is evaluated by three representative landslides with GPS monitoring datasets in Three Gorges Reservoir area, China. • The model owns low prediction error, high reliability and positive generalization ability in landslide prediction. • This study paves the potential way for realizing the real-time, whole-process and accurate landslide forecasting. [ABSTRACT FROM AUTHOR]

The Zigui basin is one of the most landslide-prone areas where thousands of landslides are distributed. Performing the landslide susceptibility mapping is a heated issue in the area. Whereas, whether the random selection of the landslides or non-landslides grid cells are reasonable in research of landslide susceptibility mapping is the existence problem nowadays. Based on the two step cluster (TSC) algorithm and the self-organizing mapping - random forest (SOM-RF) model, a novel hybrid model is proposed to overcome the above drawbacks in this study. Rough set (RS) theory is employed as an attribute reduction tool to identify the significant influencing factors of landslides, and SOM is used to produce a preliminary landslide susceptibility mapping. Two step cluster algorithm is applied in telling apart the most reasonable True-Positive (TP) from landslide grid cells in high-susceptibility zones and the False-Positive (FP) in low-susceptibility zones. Afterwards, the TP and FP are imported into the RF model as samples to produce the improved landslide susceptibility mapping. The proposed method is utilized to develop landslide susceptibility mapping in the Zigui basin. Compared with the susceptibility mapping produced by RF model, which selects landslides and non-landslides grid cells randomly, the results of TSC and SOM-RF model demonstrates to have the superior prediction skill and higher reliability in landslides susceptibility mapping. [ABSTRACT FROM AUTHOR]

Five large and many small landslides are developed in Jurassic strata along the lower reaches of Xiangxi River, where interbedded weak and hard bedrock layers foster the development of landslides with a "stair-step" sliding surface. The paper investigates the evolution characteristics of these landslides and presents a novel forecasting model for their displacements. The distribution characteristics and behavior of landslides developed along Xiangxi River is revealed by the database of landslides in the larger Zigui basin, of which this area is part. Most landslides occur at rather low elevations of <300 m and in areas of moderate rainfall. The geological evolution of landslides in the Xiangxi River valley can be divided into four stages, beginning with anticline formation, followed by valley incision, then by weathering and erosion, and culminating in formation of the colluvial landslides. The accumulative displacement curves of landslides with a stair-step sliding surface in Xiangxi River region also present obvious, step-like characteristics. A novel GA-CEEMD-RF algorithm was developed to predict the displacement of these stair-step landslides, which helps to define the combination of induced factors and weak stableness of prediction results using a single displacement prediction model and the multi-field monitoring data. • The distribution characteristics of landslides developed along lower reaches of Xiangxi River was revealed. • Interbedded weak and hard bedrock layers foster the development of landslides with a "stair-step" sliding surface. • The geological evolution of landslides in the Xiangxi River valley can be divided into four stages. • A novel GA-CEEMD-RF algorithm was developed to predict the displacement of stair-step landslides. [ABSTRACT FROM AUTHOR]