Landslide displacement prediction is one of the unsolved challenges in the field of geological hazards, especially in reservoir areas. Affected by rainfall and cyclic fluctuations in reservoir water levels, a large number of landslide disasters have developed in the Three Gorges Reservoir Area. In this article, the Baishuihe landslide was taken as the research object. Firstly, based on time series theory, the landslide displacement was decomposed into three parts (trend term, periodic term, and random term) by Variational Mode Decomposition (VMD). Next, the landslide was divided into three deformation states according to the deformation rate. A data mining algorithm was introduced for selecting the triggering factors of periodic displacement, and the Fruit Fly Optimization Algorithm–Back Propagation Neural Network (FOA-BPNN) was applied to the training and prediction of periodic and random displacements. The results show that the displacement monitoring curve of the Baishuihe landslide has a “step-like” trend. Using VMD to decompose the displacement of a landslide can indicate the triggering factors, which has clear physical significance. In the proposed model, the R² values between the measured and predicted displacements of ZG118 and XD01 were 0.977 and 0.978 respectively. Compared with previous studies, the prediction model proposed in this article not only ensures the calculation efficiency but also further improves the accuracy of the prediction results, which could provide guidance for the prediction and prevention of geological disasters.

The landslide hazard assessment plays a crucial role in landslide risk mitigation and land use planning. The result of landslide hazard assessment corrected by surface deformation, obtained through time-series InSAR, has usually proven to have good application capabilities. However, the issue lies in the uncertainty of InSAR results, where some deformations cannot be calculated, and some are not true deformations. This uncertainty of InSAR results will lead to errors in landslide hazard assessment. Here, we attempt to evaluate landslide hazards by considering combined rainfall and surface deformation. The main objective of this research was to mitigate the impact of bias and explore the accurate landslide hazard assessment method. A total of 201 landslides and 11 geo-environment factors were utilized for landslide susceptibility assessment by support vector machine (SVM) model in Wanzhou District, Three Gorges Reservoir Area (TGRA). The preliminary hazard is obtained by analyzing the statistical data of landslides and rainfall. Based on the SAR image data of Sentinel-1A satellites from September 2019 to October 2021, the SBAS-InSAR method was used to analyze surface deformation. The correlation between surface deformation and rainfall was analyzed, and the deformation factor variables were applied to landslide hazard assessment. The research results demonstrate that the error caused by the uncertainty of InSAR results can be effectively avoided by analyzing the relationship between rainfall and surface deformation. Our results can effectively adjust and correct the hazard results and eliminate the errors in the general hazard assessment. Our proposed method can be used to assess the landslide hazard in more detail and provide a reference for fine risk management and control.

Landslide-induced tsunamis in reservoirs often pose greater risks than landslides themselves. Existing studies and mitigation practices have prioritized increasing landslide stability to meet safety standards, incurring high costs and lacking sustainability. More than 5000 landslides have developed along the Three Gorges Reservoir banks in China, and some may trigger tsunamis. To effectively and sustainably manage risk of landslide-tsunami hazard chains (LTHC), we propose a new framework (based on quantitative risk analysis) involves decision-making based on risk characteristics and local economies, a strategy for selecting optimal risk control plan through cost–benefit analyses that consider long-term effectiveness and adverse effects, and dynamic management through iterative risk reassessment-recontrol cycles. The Liangshuijing landslide accelerated in 2009, posing a tsunami threat to the Yangtze River and coastal residents. Post-mitigation, the risks under four mitigation plans and combined scenarios were quantitatively evaluated. The mass removal plan reduces total risk by approximately 68%, less than the 80% reduction from the anti-slip pile plan, but with a 30-fold greater benefit–cost ratio (BCR). The surface drainage and monitoring network plans show limited long-term cost-effectiveness due to short measure lifetimes, however, their short-term BCRs exceed those of the mass removal plan due to lower direct costs. According to comprehensive long and short-term analyses, combined plans involving mass removal are the most cost-effective. Our results reveal how to select appropriate strategies for managing LTHC risk, how landslide mitigation controls tsunami risks, and how time-related parameters affect solution effectiveness. Our findings show that partially mitigating risks may provide more cost-effective and sustainable management of LTHCs than eliminating risks to negligible levels.

High cut-slopes are widespread in engineering constructions and often converted into landslides. Some extreme circumstances facilitate the landslide process, such as the weak bedding plane, rainfall, and faults. Therefore, this paper intends to offer insights into the influence of the weak bedding plane, extreme rainfall as well as faults on the landslide process of the high cut-slope. In this paper, the Anling landslide in Anhui Province, China, is selected as an example. Geological surveys, displacement monitoring, data analysis, as well as numerical simulation are carried out. The entire excavation construction and landslide deformation process are simulated to reveal the formation mechanism of the landslide using the finite difference code, FLAC3D. The effects of the fault on the landslide and the effectiveness of adjusting protection measures by adding piles are investigated on the basis of the finite difference analysis. According to monitoring data and numerical simulation, the weak bedding plane and extreme rainfall are considered the main factors leading to the Anling landslide. Field investigation and numerical experiments indicate that the fault shall facilitate and accelerate the landslide process. The construction of piles in a suitable position for the landslide is a reasonable and economical measure to stabilize the landslide.