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Rainfall with heavy intensity and long duration is a main inducing factor that triggers landslides on anti-inclined rock slopes. Numerous interbedded anti-inclined rock (IAR) slopes in Southwest China frequently experience mountain collapses and landslides under heavy rainfall. Therefore, it is necessary to study the failure mode of IAR slopes during rainfall. In this study, the Linda landslide and Mari landslide, typical IAR landslides in the study area, were used as examples to build a physical model of IAR slopes under continuous rainfall conditions, and a self-made rainfall simulation system was used to mimic natural rainfall conditions. The multi-physics data of displacement, velocity, stress, strain, pore water pressure, and acoustic signals in model deformation and failure under rainfall conditions were obtained using multi-field monitoring technology. In addition, the materials' water content and mechanical parameters at different locations of the model were obtained after the experiment. The analysis results indicate that: in the first and middle periods of rainfall, the slope model experienced a shallow local deformation and a holistic toppling deformation; until the late rain, the failure of the IAR slope caused by rainfall finally presented a typical failure mode of multiple failures and collapses of regressive landslides. According to its multi-field evolution characteristics, the velocity and acoustic signals were highly consistent with the failure characteristics of the model, which could be used as landslide early warning indicators. In addition, it was observed that the shape of the multi-slip surfaces generated by the model was a concave double straight line extending from the slope toe to the slope surface and sharing the failure section at the slope toe. Finally, the deformation and failure process of the model could be speculated to be divided into five stages based on the deformation behavior and multi-field physical data feature: the initial interlayer dislocation stage, the deformation stage, the crack propagation stage, the retrogressive failure stage, and the holistic failure stage. The failure mode and deformation mechanism of IAR slopes under rainfall were revealed in the model test, which could provide appropriate guidance for the analysis and evaluation of similar landslides. Highlights: The failure of the interbedded anti-inclined rock slope model caused by rainfall presented a typical failure mode of multiple failures and collapses of regressive landslides. The velocity and acoustic signals are highly consistent with the failure characteristics of the model, and the shape of the multi-slip surfaces was a concave double straight line extending from the slope toe to the slope surface. According to its multi-field evolution characteristics, the deformation and failure process of the model under rainfall conditions can be divided into five stages. [ABSTRACT FROM AUTHOR]

Rainfall and reservoir water level are commonly regarded as the two major influencing factors for reservoir landslides and are employed for landslide displacement prediction, yet their daily data are readily available with current monitoring technology, which makes a more refined analysis possible. However, until now, few efforts have been made to predict landslide displacements using daily data, which is likely to substantially improve accuracy and is crucial for landslide early warning. A novel feature enhancement approach for extracting critical characteristics from daily rainfall and reservoir water level data for use in landslide displacement prediction is proposed in this study. Six models, including gated recurrent units (GRUs), long short-term memory (LSTM), and support vector regression (SVR) with an unenhanced dataset and GRU-E, LSTM-E, and SVR-E with an enhanced dataset, were employed for displacement predictions at four GPS monitoring stations on the Baijiabao landslide, a typical step-like reservoir landslide. The results show that the accuracy values of all the enhanced models were significantly improved, and the GRU-E model achieved the most significant improvement, with the RMSE decreasing by 24.39% and R2 increasing by 0.2693, followed by the LSTM-E and SVR-E models. Further, the GRU-E model consistently outperformed the other models, achieving the highest R2 of 0.6265 and the lowest RMSE of 16.5208 mm, significantly superior than the others. This study indicates the feasibility of improving the accuracy of landslide monthly displacement predictions with finer monitoring data and provides valuable insights for future research. [ABSTRACT FROM AUTHOR]

Data from in situ monitoring of displacement is of fundamental importance, not only in hazard assessments, but also for understanding the deformation mechanisms of landslides. However, acquiring reliable and continuous displacement data from complex landslides is often a problem because of the high cost of instruments and maintenance. Among more than 250 dangerous landslides monitored in the Chinese Three Gorges reservoir area, the most systematic and complete monitoring data have been obtained from the Shuping landslide. A ten-year record of GPS displacements measured in eight different locations, plus rainfall and reservoir level data, are utilised to identify the movement characteristics and causal factors of the Shuping landslide. The cumulative displacement curves show a series of abrupt increases that occur every year and are separated by periods of steady, slower deformations. The maximum cumulative displacement that occurred during the ten-year monitoring period was 4.7 m at site SP-2. The maximum displacement rate was 35 mm/day, which was observed on June 13, 2012 at site ZG86. Causal factors of the Shuping landslide are identified through qualitative comparisons of the monitoring data and cross-examined by grey correlation degree analysis. The results show that reservoir water level drawdown is the main factor accelerating the movement of the Shuping landslide, with heavy rainfall playing a subordinate role. The proposed use of field data and analytical methods has the potential for providing procedures and solutions for the interpretation of landslide monitoring data. [ABSTRACT FROM AUTHOR]

In this study, a data mining approach is proposed to investigate the hydrological causes of the Majiagou landslide, located in the Three Gorges Reservoir in China. It is possible to determine the cause-and-effect relationships between hydrological parameters and landslide movement. The data mining approach consists of two steps: first, hydrological indicators and landslide movements are discretized using the two-step cluster analysis; second, the association rule mining with the Apriori algorithm is employed to identify the contribution of each hydrological parameter to landslide movement. The results obtained suggest that deformation and later failure occurred first at the toe of the landslide and progressed upslope due to rising water level in the reservoir, prolonged heavy rainfall, and rapid drawdown in the reservoir. The proposed novel use of field data and data mining has the potential for providing procedures and solutions for an effective interpretation of landslide monitoring data. [ABSTRACT FROM AUTHOR]

The Huangtupo landslide is the volumetrically largest, most complex, and economically most significant landslide in the Three Gorges Reservoir Region. Detailed information about the character and dynamics of this tractive landslide is available because of access provided by a unique, 908-meter in situ research tunnel, augmented by long-term data provided by an extensive monitoring system for ground water levels, rainfall, and in situ deformation. Three major rupture surfaces plus 12 interlayer sliding zones (weak layers) were recognized during tunnel excavation, which also provided access and samples that establish a complex, multistage deformation history. Data from the in situ monitoring system shows that the toe of the landslide is creeping at a steady annual rate of about 25-30 mm, but at a slower rate of about 12 mm/year near the crown. However, the monthly creep rates are variable, being highest during May-August when rainfall is heavy and reservoir levels are low. Displacement-depth curves in the HZ6 borehole inclinometer establish that the creep of Slump Mass I# is most rapid above a depth of 37 m, where a major rupture zone occurs immediately above bedrock. Engineered defense structures along the toe of Huangtupo landslide can improve its stability to some extent. The effect of rapid changes of the reservoir level on landslide stability should be considered in reservoir management. [ABSTRACT FROM AUTHOR]

In recent years, a large number of bank destruction occur in the reservoir area under the effect of water fluctuation, which may be lead to reservoir accumulative landslide geological hazards finally. The paper conducted the bank destruction forecasting study for accumulative landslides in the Three Gorges Reservoir Region, China utilizing back-propagation (BP) neural network approach. A representative scenario of Jinle landslide is then taken for analysis purposes. On the basis of the existing data sets of bank destruction cases, the BP neural network forecasting model and the corresponding programs for bank destruction are both presented, whose forecasting result is validated by two independent approaches, namely empirical method and numerical modeling method. Furthermore, the BP neural network model had obvious advantages over the convention approaches in the aspects of the fast calculation speed and high convenience. According to the bank destruction forecasting scale presented above, the corresponding revetment measures can be proposed to prevent the occurring of the bank destruction, whose effectiveness has been further validated by the actual engineering practice. [ABSTRACT FROM AUTHOR]

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD-ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD-ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China. [ABSTRACT FROM AUTHOR]