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Based on the machine learning algorithms, prediction models for landslide displacement with step-like behavior in the reservoir area were established for landslides prevention and reduction; these models could predict a given test set very well. However, due to the length and the sequence of the training set in prediction models, the predictive ability of these prediction models could not be evaluated accurately if just validated with a given test set. To solve the problem, a hybrid reliability model was proposed. Complimentary ensemble empirical mode decomposition (CEEMD) algorithm was used to decompose the accumulated displacement into the trend displacement and the periodic displacement firstly. The Gauss function was used to predict the trend displacement, and the random forest (RF) and back propagation neural network (BPNN) algorithms were employed to predict the periodic displacement. Furthermore, a novel performance function for the reliability analysis of the displacement prediction model was derived to address failure probabilities in different cases. The Baijiabao landslide in the Three Gorges Reservoir Area was taken as an example for reliability analysis of the prediction model for landslide displacement with step-like behavior, and the predictive ability of the CEEMD-RF model and the CEEMD-BPNN model were compared. The results indicated that the CEEMD-RF model and CEEMD-BPNN model both can accurately predict the accumulated displacement of the given test set; the predictive value obtained with the CEEMD-RF model or the CEEMD-BPNN model showed uncertainties of the prediction model for landslide displacement and the predictive ability of the CEEMD-RF model was more reliable than the CEEMD-BPNN model under different cases. The failure probability proposed in the paper could evaluate the predictive ability of the model more accurately and comprehensively compared with the existing assessment indices. [ABSTRACT FROM AUTHOR]

An increasing number of landslides are occurring during the construction of highways in mountainous areas all over the world. A recent highway landslide located in the mountainous region of Southwest China is taken as a representative example to carry out the initiation mechanism and deformation characteristics analysis, and study the intelligent partitioning method in this paper. The geological conditions of the landslide are obtained through detailed geological surveys and investigation. A three-dimensional (3D) monitoring network with a total of 72 multi-points, including 53 surface displacement monitoring points and 19 deep displacement monitoring holes, is established to examine the spatial deformation characteristics of the landslide for nearly 25 months. The monitoring results of the surface displacement show that the dynamic deformation of the landslide can be divided into three stages, namely initial deformation stage, accelerated deformation stage, and stabilization stage. Based on the strata distribution obtained by the borehole investigation and the results of deep displacement monitoring, the depth of the slip surface can be reliably determined. The smart contract method in blockchain technology combined with the multi-point monitoring dataset is novelty applied to realize the intelligent automatic partition of the highway landslide without any human involvement. This method can provide the basis for further independent analysis of different landslide zones. According to the investigation and analysis, the initiation mechanism for the highway landslides is attributed to both internal poor geological conditions and external intense precipitation and engineering excavation. [ABSTRACT FROM AUTHOR]

Jurassic facility-sliding strata have been identified as a fundamental factor affecting the occurrence of rainfall-reservoir induced landslides in western Hubei Province, China Three Gorges Reservoir area. Regional landslide susceptibility mapping is the most effective method for landslide prediction and mitigation. To solve the current problem of identifying the true landslides and non-landslides, a novel hybrid model based on the two steps self-organizing mapping-random forest (two steps SOM-RF) algorithm is proposed. The identified high and very high susceptibility zones are located within the hydro-fluctuation belt and regions with low altitude based on the datasets before 2014. Deviation and variance of other ten datasets are generated to evaluate the reliability of the maps for the problem of unbalanced sample sizes. Two typical landslides occurred in 2017 have been found in the very high susceptibility zone, which emphasized the validity of susceptibility mapping. To verify the effectiveness of selecting true landslides and non-landslides based on two steps SOM model, recorded landslides and non-landslides randomly chosen from landslide-free areas are put into the single RF model for comparison. The receiver operating characteristic curve and Accuracy index are applied to compare the performance of the landslide susceptibility maps created based on two steps SOM-RF and the single RF model. The results demonstrate that the consideration of true landslides and non-landslides is effective in producing a more accurate landslide susceptibility map with superior prediction skill and higher reliability. [ABSTRACT FROM AUTHOR]

Due to the complex and non-linear characteristics of landslide evolution, the deformation law of landslides is always hard to be predicted. Considering the dynamical evolution process of landslide physical mechanism, a Physics-Informed Data Assimilation (PIDA) method is first proposed for landslide displacement forecasting. It can enhance the prediction ability of the physical forecasting model, and solves the problem of ignoring the physical significance of landslides in mathematical prediction model. Additionally, the superiority of Particle Filter (PF) in nonlinear and non-Gaussian system determines its application in complex problem. Aiming at the hydrodynamic pressure-driven landslide, the Sliding Zone Deterioration (SZD) model is constructed as the state equation of the PF algorithm. And the observation equation is formed by the relationship between landslide deformation and monitoring datasets. The physics-informed landslide displacement prediction model serves as a bridge, consisting of the shear strength parameter dynamic updated model and physical mechanism analysis model, for key parameters feedback, modification, and update. Comparing the results to traditional Long Short-Term Memory (LSTM) and Back Propagation Neural Network (BPNN) methods, the PIDA method combining triggering and instability mechanisms performs better in accuracy. Moreover, it enables dynamic and reliable landslide deformation prediction. [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]

Abstract: Three reconstruction methods based on Tikhonov regularization are used to solve the ill-posed inverse problem when incorporating Monte Carlo calculations into the β-ray induced X-ray spectrometry (BIXS) for analyzing the tritium content and depth distribution in the material surface. These methods are implemented by the GENEREG, CONTIN and REGUTOOLS codes, respectively. The tritium sample is assumed to be a molybdenum slab of 5μm thickness, and an argon gas of 5mm thickness is filled in front of the molybdenum slab. The Monte Carlo code PENELOPE is used to simulate the X-ray energy spectra induced by tritium’s β-decays. The reconstruction results for tritium depth distributions by these three codes are compared and the comparison shows that the code GENEREG can give satisfactory results. Some discussions are made. [Copyright &y& Elsevier]

Multi-layer slopes are widely found in clay residue receiving fields. A generalized horizontal slice method (GHSM) for assessing the stability of multi-layer slopes that considers the energy dissipation between adjacent horizontal slices is presented. In view of the upper-bound limit analysis theory, the energy equation is derived and the ultimate failure mode is generated by comparing the sliding surface passing through the slope toe (mode A) with that below (mode B). In addition, the influence of the number of slices on the stability coefficients in the GHSM is studied and the stable value is obtained. Compared to the original method (Chen's method), the GHSM can acquire more precise results, which takes into account the energy dissipation in the inner sliding soil mass. Moreover, the GHSM, limit equilibrium method (LEM) and numerical simulation method (NSM) are applied to analyze the stability of a multi-layer slope with different slope angles and the results of the safety factor and failure mode are very close in each case. The ultimate failure modes are shown to be mode B when the slope angle is not more than 28°. It illustrates that the determination of the ultimate sliding surface requires comparison of multiple failure modes, not only mode A. [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]

The paper develops an analytical method to design the plane arrangement of stabilising piles under spatial lateral load of 3D colluvial landslides with multilayer sliding masses. Firstly, an approach based on the geological map and binary Lagrange Interpolation method was presented to establish 3D landslide calculation models. The improved 3D limit equilibrium method was then proposed to calculate the stability coefficient and spatial distributed lateral load of landslide with multilayer sliding masses. And main sliding direction of 3D multilayer landslide can be determined by the developed principle of minimum potential energy. Moreover, through assuming the piles and soil as elastic transversely isotropic continuum medium, analytical approach was employed to accurately calculate the stress distribution of soil between adjacent piles; and the maximum pile spacing can be calculated. Finally, the reasonable arrangement of stabilising piles can be determined based on Monte-Carlo method and cost of all piles. The finite difference method was employed to validate the theoretical results through the case study of Bazimen landslide. And it is found that the proposed method for the design of piles' arrangement is economically beneficial compared with the traditional method on the premise of ensuring safety. [ABSTRACT FROM AUTHOR]

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]

Abstract: An enzyme incubation–water extraction (EI–WE) method was developed and optimised for the extraction of the natural antioxidant taxifolin and of the total flavonoids from wood sawdust of Larix gmelini (Rupr.) Rupr. A factorial design and a central composite design approach were used for method optimisation. Optimal conditions were 0.5mg/ml cellulase and 0.5mg/ml pectinase, a pH of 5.0, a temperature of 32°C and 18h incubation time. The flavonoids and taxifolin were extracted in hot water at 50°C for 30min, with a solid to liquid ratio of 1:20. Under optimised conditions, the yields of taxifolin and total flavonoids increased from 1.06±0.08 to 1.35±0.04mg/g and 4.13±0.17 to 4.96±0.29mg/g, respectively. DPPH and BHT assays revealed that the EI–WE samples had 1.8- and 1.68-fold higher antioxidant activities than the controls. SEM results revealed the structural disruption of wood sawdust with enzyme incubation. [ABSTRACT FROM AUTHOR]

Abstract: Monte Carlo calculations, instead of the analytical method, are incorporated into the β-ray induced X-ray spectrometry for analyzing the tritium content and depth distribution in the near surface. This improvement makes the β-ray induced X-ray spectrometry for tritium analysis be able to take into account the physics processes more accurately and be utilized more flexibly. The inverse problem involved is treated by using the Tikhonov regularization method and classical molecular dynamics method. The results concerned are discussed. [Copyright &y& Elsevier]

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]

The primary objective of this research is to better understand the movement characteristics and patterns of a sudden landslide that occurred in the Three Gorges Reservoir, China (the Sept. 2, 2014 Shanshucao landslide) by using three periods of multi-source remote sensing images in combination with a field survey. A georeferenced, aerophotographic orthomosaic and corresponding 1:2000 topographic map of the pre-landslide area was referenced to rectify the micro-unmanned aerial vehicle (UAV) based orthomosaic, digital surface model (DSM) and Google Earth satellite image of the post-landslide area. By using orthomosaics of the post-landslide area, especially the very high spatial resolution micro-UAV based orthomosaic, the landslide boundaries were accurately delineated. By comparing the images of the area both pre- and post-landslide, three landslide subzones were identified, and two boundaries between the three subareas were divided more precisely than in previous studies. Based on the digital elevation model (DEM) of the pre-landslide area and DSM of the post-landslide area, the change in altitude and volume of the landslide were quantified. Then, the movement pattern of the landslide was proposed. The results showed the rocky, southern sliding part was the main sliding zone with the longest distance and deepest sliding surface, and the northern soil mass did not experience any overall movement. However, loose soil mass collapse and features of counterclockwise rotation were observed under the traction of the rocky, southern sliding part. Additionally, more accurate knowledge has been obtained including the location of the original lateral boundary, shear outlet altitudes, and main triggering factors of the rocky sliding mass. This study has demonstrated that the use of multi-source remote sensing images, which include both pre- and post-landslide images, can effectively describe the characteristics, patterns, and even the mechanism of a sudden landslide. [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]