Since the impoundment of Three Gorges Reservoir (TGR) in 2003, numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall. One case is the Outang landslide, a large-scale and active landslide, on the south bank of the Yangtze River. The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics. Data mining technology, including the two-step clustering and Apriori algorithm, is then used to identify the dominant triggers of landslide movement. In the data mining process, the two-step clustering method clusters the candidate triggers and displacement rate into several groups, and the Apriori algorithm generates correlation criteria for the cause-and-effect. The analysis considers multiple locations of the landslide and incorporates two types of time scales: longterm deformation on a monthly basis and short-term deformation on a daily basis. This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors. The data mining results reveal different dominant triggering factors depending on the monitoring frequency: the monthly and bi-monthly cumulative rainfall control the monthly deformation, and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide. It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslideprone areas. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Under the heavy rainfall risk due to global warming, a new trend has emerged in geological disasters of loess, which have often evolved into a chain form of disaster chain of loess (DCL) in recent years. The DCL is characterized by multiple, hidden, catastrophic, and complex characteristics that seriously affect the construction and operation of large-scale infrastructure on the Loess Plateau. To understand the formation mechanism of a disaster chain of loess, we took the Shiyangpo DCL, a typical disaster chain occurring recently on the Loess Plateau, as an example to investigate the geomorphic features and deformation characteristics of DCL using new technologies and methods such as Unmanned Aerial Vehicle (UAV) mapping and Geographic Information Systems (GIS) spatial analysis technology. A series of special laboratory tests considering the vibration of the loess subgrade was conducted to explore the changes in physical and mechanical properties of loess samples in the study area under natural, saturated, and vibration conditions. Additionally, the trigger factors and evolution process of this DCL were analyzed, and the formation mechanism of recently emerging typical DCL was revealed as well. The triggering factors of the disaster were summarized as follows: loess nature, heavy rainfall, irrigation, irrational excavation, incomplete drainage channels, and long-term vehicle vibration of roadbeds. Furthermore, extreme rainfall was identified as the primary inducing factor of Shiyangpo DCL. Finally, the development and evolution of Shiyangpo DCL were divided into five stages: the formation of the loess sinkhole stage, the occurrence of the loess subsidence stage, the occurrence of the loess collapse stage, the occurrence of the loess landslide stage, and the formation of river -blocking and dammed lake stage. This study reveals the cause and evolution process of the newly emerged DCL in the Loess Plateau, and the new techniques and methods involved can provide references for the theoretical research and prevention of loess geological disasters in other places.

Temperature changes may cause irregular soil uplift or thawing settlements in frozen soil areas, potentially affecting the safe operation of High-Speed Railways (HSR). Analyzing and predicting these deformation characteristics is thus critical. However, the conventional forecasting and analysis techniques rarely considered factors such as dynamic parameter variations, uncertainties, and measurement errors, which hinder accurate regional scale forecasting. To bridge this gap, this paper introduces a novel time-series coupling method, which integrates post-processing deformation from Interferometric Synthetic Aperture Radar (InSAR) with a frost heave model (FHM), facilitated by the ensemble Kalman filter (EnKF) assimilation algorithm. We obtained deformation observations along the HSR using Persistent Scatterer InSAR (PS-InSAR) technology in combination with time series post-processing techniques. Considering the causative factors for deformation, we structured the FHM. By integrating FHM with observational data using the EnKF algorithm achieved an efficient upgrade of the posterior distribution of model parameters. This integration significantly improves the predictive accuracy, it facilitates an efficient update to the posterior distribution of model parameters, leading to enhanced prediction accuracy of our model. Our experimental results indicate that the effectiveness of this approach, with observational data assimilation into FHM reducing the average Root Mean Square Error (RMSE) to a mere 0.247 mm. Concurrently, both the Normalized Reduction Error Index (NER) and the Assimilation Efficiency Factor (EFF) values surpassed 0.60 and 0.84 respectively. These underlines signify a successful update of our model parameters, which in turn elevates the accuracy of future deformation predictions, thereby promising safer railway operations.

The present study deals with the field observations and results obtained from the Geotechnical investigations and Ground Penetrating Radar survey carried out on Thatri landslide. Thatri township is situated on the left bank of the Chenab River along NH-244, 43 km from district headquarter Doda, Jammu and Kashmir. The landslide occurred on 2nd February, 2023 damaged 21 buildings, affected about 150 people and created situation like Joshimath tragedy. Detailed field investigations on lithology, sub-surface structure determined by GPR investigations and geotechnical parameters of the soil revealed that the leading edge of the slide caused most damage due to cracks developed in the slope wash deposits followed by subsidence and down slope movement of a portion of the township (Nai Basti). The study revealed that percolation of water from soak pits, sewerage, and rain water into the terrace deposits comprising of clays, silt, sand and gravels was the major triggering factors.