This paper presents a new method for a regional-scale rainfall-induced landslide early warning system (LEWS) based on the outputs of the "Fast Shallow Landslide Assessment Model" (FSLAM), a physically based model used to compute slope stability at a regional scale. The LEWS combines landslide susceptibility and rainfall thresholds to depict the areas that are prone to slope failures and issues qualitative warnings over the study area. Both the susceptibility map and the rainfall thresholds were obtained based on the outputs from running FSLAM with 25 different rainfall scenarios. The final output of the LEWS is a slope-unit-based map. The LEWS was implemented for Cili County, Hunan Province, China, and tested for the year 2020. The warning level stayed "Low" during most of the year. High warnings were issued during the summer and were either due to intense rainfall events or abundant long-duration precipitation. The LEWS was able to issue appropriate warnings corresponding to the time and location of three known landslides that occurred in the study area in 2020. Although long-term validation with more landslide data and improved geotechnical data is needed to reduce the LEWS uncertainties, this approach is promising and could support authorities managing landslide risk.

The grid units are assumed to independent in grid-based landslide susceptibility prediction (LSP). However, interactions exist due to the continuity and correlation of landslide environmental factors. Hence, the influences of spatial correlation among these factors are considered in the study. The domain analysis in ARCGIS 10.2 software is adopted to calculate the mean, standard deviation and range of fifteen environmental factors. Then, it incorporates spatial correlation into the dataset. Three machine learning models, such as support vector machine (SVM), C5.0 Decision Tree (C5.0 DT) and random forest (RF), are utilised to establish spatial relationship models. Subsequently, prediction performance is evaluated with the receiver operating characteristic (ROC) curve, frequency ratio accuracy and statistical indices of landslide susceptibility indexes (LSIs). Results show: Models considering spatial correlation outperform those that do not, with an increase in area of ROC curve (AUC) values by 2-6%, frequency ratio accuracy by 0.7-1.3 and smaller LSI mean and larger standard deviation. Moreover, the spatial correlation-RF model had the highest accuracy with AUC of 94.35%. The LSP accuracy can be enhanced by considering spatial correlation of environment factors.

Landslides are among the most prevalent and hazardous geological disasters worldwide. In particular, their frequency and severity are significant in China. Traditional landslide prediction models often struggle to address the phased and abrupt displacement patterns of step-type landslides. These landslides undergo significant deformation during the rainy season, while exhibiting relatively mild changes during non-rainy periods. This seasonal variability makes traditional static models inadequate for capturing complex, nonlinear dynamic processes. To overcome these limitations, this study proposes a novel landslide displacement prediction method based on the VMD-Mamba model. The Variational Mode Decomposition (VMD) algorithm decomposes the original displacement data into trend and periodic components, effectively capturing trend and periodic variations. The Mamba model uses the time-series information in the data to build a predictive model. This integrated approach enhances the model's ability to predict sudden displacement changes and offers a robust solution for step-type landslide prediction, addressing the challenges posed by nonlinear and dynamic processes in landslide monitoring. The results indicate that the proposed model achieves a landslide displacement prediction fit (R2) greater than 0.97. During periods of rapid deformation, its predictive performance significantly surpasses other intelligent forecasting methods such as IPSO-LSTM model, CNN-LSTM model and transformer models. The main conclusion of this study is that the VMD-Mamba model effectively captures the dynamic nonlinear characteristics of landslide displacement, offering a novel approach to improving landslide prediction accuracy. This advancement holds significant potential for application in landslide early warning and disaster prevention, providing a valuable tool for landslide risk management on a global scale.

ObjectiveWith the construction of reservoir projects, the problem of deformation and instability of colluvial landslides in reservoir areas has become increasingly prominent. These landslides often experience deformation during the period of reservoir water level decline particularly exacerbated by rainfall. Therefore, the seepage-deformation mechanism of landslides under the joint action of reservoir water level decline and rainfall is one of the key scientific issues.MethodsIn this paper, a typical colluvial landslide in the Three Gorges Reservoir Area, the Shilongmen landslide, is used as a prototype. Physical model tests were designed to observe the macroscopic phenomena of the slope body and the pressure changes in the slope during combined scenarios of reservoir level drops and rainfall of different intensities, revealing the seepage-deformation mechanism of the colluvial landslide under complex hydrological conditions.ResultsThe test results indicated that the decrease in reservoir level has a more obvious effect on the control of seepage at the front of the slope, while rainfall significantly raises the water head at the middle and rear of the slope, and the combination of the two will increase the hydraulic gradient in the slope, thus inducing slope deformation.ConclusionThe seepage-deformation mechanism of the colluvial landslide when the reservoir level drops is as follows: In the absence of rainfall, only shallow surface scouring action occurs due to the difference between the internal and external heads of the slope. Under the condition of the worst rain in a 100-year rainstorm, cracks appear near the high water level, and then overall deformation occurs, with a maximum displacement of 58.3 mm. This study can provide a theoretical basis for the prevention and control of colluvial landslides under complex hydrological conditions.

Typhoons are recurring meteorological phenomena in the southeastern coastal area of China, frequently triggering debris flows and other forms of slope failures that result in significant economic damage and loss of life in densely populated and economically active regions. Accurate prediction of typhoon-triggered debris flows and identification of high-risk zones are imperative for effective risk management. Surprisingly, little attention has been devoted to the construction of physical vulnerability curves in typhoon-affected areas, as a basis for risk assessment. To address this deficiency, this paper presents a quantitative method for developing physical vulnerability curves for buildings by modeling debris flow intensity and building damage characteristics. In this study, we selected the Wangzhuangwu watershed, in Zhejiang Province of China, which was impacted by a debris flow induced by Typhoon Lekima on August 10, 2019. We conducted detailed field surveys after interpreting remote sensing imagery to analyze the geological features and the mechanism of the debris flow and constructed a comprehensive database of building damage characteristics. To model the 2019 debris flow initiation, entrainment, and deposition processes, we applied the Soil Conservation Service-Curve Number (SCS-CN) approach and a two-dimensional debris flow model (FLO-2D). The reconstructed debris flow depth and extent were validated using observed debris flow data. We generated physical vulnerability curves for different types of building structures, taking into account both the degree of building damage and the modeled debris flow intensity, including flow depth and impact pressure. Based on calibrated rheological parameters, we modeled the potential intensity of future debris flows while considering various recurrence frequencies of triggering rainfall events. Subsequently, we calculated the vulnerability index and economic risk associated with buildings for different frequencies of debris flow events, employing diverse vulnerability functions that factored in uncertainty in both intensity indicators and building structures. We observed that the vulnerability function utilizing impact pressure as the intensity indicator tends to be more conservative than the one employing flow depth as a parameter. This comprehensive approach efficiently generated physical vulnerability curves and a debris flow risk map, providing valuable insights for effective disaster prevention in areas prone to debris flows.

Objective To investigate the spatial-temporal variations in landslide susceptibility due to human engineering activities in resettled urban areas. Methods This study focuses on the new urban area of Yunyang County in the Three Gorges Reservoir region. Landslide susceptibility time-varying index factors were introduced to map spatial-temporal susceptibility differences and explore the spatial-temporal evolution of landslide disasters during urbanization in resettled urban areas. First, the stacking ensemble model was selected as the static susceptibility evaluation model. Then, the InSAR deformation rates and land use types over three distinct time spans (namely, January 16, 2017, to August 27, 2018 (T1), September 20, 2018, to July 30, 2021 (T2), and August 23, 2021, to November 17, 2023 (T3)) were selected as time-varying factors. Last, the time-varying factors were combined with the static evaluation results to create susceptibility difference distribution maps for the different periods. Results The study revealed that introducing time-varying factors in the analysis of spatial-temporal susceptibility differences effectively reflects the impact of urbanization on landslide disasters. When the land type in the study area changed from non-engineering land to engineering land, the landslide susceptibility level generally increased, with grid shares of 61.3% and 67.1% in the two change stages, respectively. The temporal trends of the InSAR displacement time series curves for selected typical landslides in urban areas showed high spatial-temporal correlations with land type changes, further validating the reliability of this method. Conclusion The proposed research approach provides the basis for disaster prevention, mitigation, and regional planning during the urbanization process in resettled urban areas of the Three Gorges Reservoir region.

The rapid movement and extensive displacement of gravel-silty clay landslides result in significant property damage and loss. Following the destabilization of the Shaziba landslide in Enshi City, it transformed into a debris flow, ultimately obstructing the Qingjiang River and creating a barrier dam. This study delves into the failure mechanism, leap dynamics, and motion processes of this specific landslide by employing a blend of ring shear testing and the discrete element method. Initially, the residual shear strength of the sliding soil was assessed through ring shear tests conducted under various coaxial stresses and shear rates within the sliding region, using field surveys and aerial imagery. Building upon this foundation, the entire progression of the landslide-from sliding to settlement-was replicated using PFC3D, allowing for an examination of the landslide's movement characteristics such as speed, displacement, and trajectory. The findings indicate that the shear displacement and residual friction coefficients are higher at elevated shear rates compared to lower rates. The landslide commences with an initial acceleration phase, with the silty clay material's movement lasting approximately 757 s, reaching a maximum velocity of 32.5 m/s and a displacement exceeding 1000 m. The simulated settlement volume of the landslide (9.31 × 105m3) closely aligns with the results obtained from field investigations (1.5 × 106m3). This research offers comprehensive insights into recent Shaziba landslides, serving as a valuable resource for enhancing our understanding of the dynamics involved and mitigating the potential risks associated with such events.

Recently, the positive unlabeled (PU) learning algorithms have proven highly effective in generating accurate landslide susceptibility maps. The algorithms categorize samples exclusively into positive samples (landslides) and unlabeled samples for training, eliminating random or subjective selection of non-landslide samples. However, existing PU learning algorithms face limitations in capturing correct negative samples in multi-genesis landslide areas, leading to lower prediction accuracy. To address this issue, the PU-pullbaggingDT algorithm was proposed in this study. This integrated method combines the strengths of two techniques: the superior ranking performance of the PU-baggingDT algorithm and the low bias of the contrastive learning approach. The contrastive learning introduces a new contrastive loss of PU learning (PUpull loss), which pulls the distance of similar landslide samples in the projection space closer based on the validation accuracy threshold, without the need for data augmentation and class probability. The PUpull loss relaxes the tightness toward unlabeled samples, reducing the impact of incorrectly defined non-landslide samples on prediction results in multi-genesis landslide areas. The proposed algorithm outperforms existing PU-learning and machine learning methods (support vector machine, decision tree, logistic regression, AdaBoost, and XGBoost) with random selection of negative samples for predicting landslide susceptibility in China's Zigui County, as demonstrated by comprehensive evaluation metrics. The landslide susceptibility mapping utilizes equal interval division and ranking, achieving approximately a 90% landslide percentage in areas with very high and high susceptibility in Zigui County. This demonstrates the capability of the proposed algorithm to effectively predict landslide susceptibility in complex geological settings.

Susceptibility evaluation is the basis of regional landslide risk early warning and stability analysis. Scientific and reasonable division of evaluation unit is the key to landslide susceptibility evaluation. For large-scale fine landslide susceptibility evaluation, the traditional slope unit division method based on hydrology and geomorphology generally results in low accuracy of the evaluation. In this paper, an improved slope unit method based on the slope geological environment is proposed. Dazhou Town was selected as an example and the obtained results from the proposed model were compared with the results from hydrological analysis method and curvature watershed method. The results show that the size uniformity of the evaluation units divided by the proposed method is better, and no fine units or deformed long strip units were generated. The overall morphological characteristics of the evaluation unit are more reasonable, and the morphological index is between 1 and 2, which generally presents circular-like or square-like shape. At the same time, the superposition degree between the results of the improved slope unit division and the range of the existing disaster boundary is the highest, which can better reflect the physical significance of landslide risk assessment. The proposed model has significant potential for improving the accuracy of regional landslide susceptibility evaluation.

Typhoon debris flows are recurrent phenomena with a high capacity to cause significant economic and life loss in the coastal areas. Accurately predicting the movement process and determining the potential zones and risk assessment are crucial to design mitigation strategies and to reduce societal and economic losses. In this study, the Wangzhuangwu (WZW) gully was chosen as the study object, which once broke out a debris flow induced by the Typhoon Likima on 10 August 2019. First, a detailed field investigation and interpretation of remote sensing imagery were carried out to study the trigger mechanism and quantify the characteristics of the debris flow. Second, the movement and deposition process of the 2019 WZW debris flow were reconstructed based on the Soil Conservation Service-curve number (SCS-CN) approach and a two-dimensional finite model (FLO-2D PRO model). The debris flow inundation and evolutionary trajectory were shown to be reasonably comparable with historical debris flows. Then, the potential hazard zones of debris flows with different recurrence intervals were determined based on the validated rheological parameters. Here we established a two-factors model that couples maximum flow depth with momentum to classify the hazard zones. Finally, we calculated the vulnerability distribution and economic risk of the buildings with different recurrence intervals based on a quantitative risk formula. This study provides a complete and efficient mean to determine the values of debris flow parameters and to implement a hazard and risk assessment based on numerical simulation. This proposed approach efficiently generated a debris flow risk distribution map that can be used for effective disaster prevention in the debris flow-prone areas.

Abstract. Rockfall hazard is frequent along the national road (G318) in west Hubei, China. To understand the distribution and potential hazard probability, this study combines the result of a 3-years engineering geological investigation, statistical modeling, and kinemics-based method to identify risky road sections. Rockfall hazard probability is calculated by integrating spatial, temporal, size probability, and reaching probabilities of source areas. Rockfall source areas are preliminarily identified first by slope angle threshold (SAT) analysis. Random Forest model (RFM) and multivariate logistic regression model (MLRM) are then applied and compared to get the final susceptible source areas, considering eight factors, including slope, aspect, elevation, lithology, joint density, slope structure, land-use type, distance to the road. Temporal and size probability of source areas are separately obtained by Poisson distribution and power-law distribution theory. An important parameter (reach angle) for rockfall trajectory simulation was determined by back analysis in Flow-R and validated by field investigation. The results show good fitness with the measurements by field investigation. In the conditions of 5, 20, and 50 years return period, potential risky road sections are found out under two size scenarios (larger than 1 000 m3, 10 000 m3). This research helps the local government to completely understand the rock falls from source area existence and potential risk to roads.

降雨触发滑坡机制是开展滑坡灾害气象预警、风险评价和工程治理的关键科学问题.选择三峡库区巴东燕子滑坡作为典型实例,设计制作滑坡物理模型,通过设置3种强降雨工况,实时监测滑坡不同位置土压力、孔隙水压力和含水率数据,结合数值模拟与堆积层滑坡动力学理论分析,探讨了厚层堆积层滑坡在强降雨条件下的变形特征与破坏机制.试验表明:强降雨条件下滑坡变形始发于坡体上部地形转折处的后缘裂隙;强降雨导致滑坡内部土压力、孔隙水压力和含水率不同程度上升,且滑带处的上升幅度明显大于滑坡浅表处;100 mm/h极端降雨结束后,滑坡开始缓慢蠕滑,随后经历加速、短暂减速、再次加速下滑直至滑移停止的破坏演化过程.滑坡触发机制为:降雨初期,坡表以孔隙流入渗为主,后缘裂隙的形成构成了雨水入渗的优势渗流通道,雨水入渗造成滑坡地下水位上升,坡脚冲刷垮塌导致滑坡前缘出现渗流排泄点,产生动水压力,同时滑带在长时间浸泡软化作用下强度持续降低.滑坡最终在滑带剪切破坏下发生了整体推移式滑动.

Landslide susceptibility mapping (LSM) is significant for landslide risk assessment. However, there remains no consensus on which method is optimal for LSM. This study implements a dynamic approach to landslide hazard mapping by integrating spatio-temporal probability analysis with time-varying ground deformation velocity derived from the MT-InSAR (Multi-Temporal InSAR) method. Reliable landslide susceptibility maps (LSMs) can inform landslide risk managers and government officials. First, sixteen factors were selected to construct a causal factor system for LSM. Next, Pearson correlation analysis, multicollinearity analysis, information gain ratio, and GeoDetector methods were applied to remove the least important factors of STI, plan curvature, TRI, and slope length. Subsequently, information quantity (IQ), logistic regression (LR), frequency ratio (FR), artificial neural network (ANN), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) methods were performed to construct the LSM. The results showed that the distance to a river, slope angle, distance from structure, and engineering geological rock group were the main factors controlling landslide development. A comprehensive set of statistical indicators was employed to evaluate these methods’ effectiveness; sensitivity, F1-measure, and AUC (area under the curve) were calculated and subsequently compared to assess the performance of the methods. Machine learning methods’ training and prediction accuracy were higher than those of statistical methods. The AUC values of the IQ, FR, LR, BP-ANN, RBF-ANN, RF, SVM, and CNN methods were 0.810, 0.854, 0.828, 0.895, 0.916, 0.932, 0.948, and 0.957, respectively. Although the performance order varied for other statistical indicators, overall, the CNN method was the best, while the BP-ANN and RBF-ANN method was the worst among the five examined machine methods. Hence, adopting the CNN approach in this study can enhance LSM accuracy, catering to the needs of planners and government agencies responsible for managing landslide-prone areas and preventing landslide-induced disasters.

This paper proposes a multidimensional landslide warning method based on statistical and physical models. Firstly, the least square linear fitting (LSF), quantile regression (QR), and logistic regression (LR) methods are used to establish the cumulative rainfall-duration-mean intensity (E-D-I) threshold models based on the rainfall landslide events in Sangzhi County, Hunan Province, from 1987 to 2007, which improve the threshold precision compared with traditional I-D analysis. Then, the thresholds are quantitatively compared using list skill scores and receiver operating characteristic (ROC) curves. The results show that the skill score of T-LR for E-D performed the best, indicating that the corresponding threshold equation is the most suitable for Sangzhi County. Further, the improved SINMAP model is used to analyze the slope stability for Liyuan, Sangzhi County, under four rainfall return periods of 5, 10, 20, and 50 years. By the relationship between rainfall and landslide instability probability, the rainfall threshold inducing the landslide in Liyuan Town is determined to be 120 mm/day. Finally, the landslide events from 2008 to 2017 in the SINMAP model are extracted to verify the appropriateness of the threshold equation. At least 75% (in fact, 100% of T-LR for E-D) of the landslide events are above all thresholds of 0.5 percentile. The converted I-D thresholds share similar trends compared with similar working areas globally. The multidimensional threshold proposed can provide a theoretical basis for preventing and managing landslide disasters in Sangzhi County, which is of significant academic value and has practical implications.

Many landslides have been reactivated along the banks of the Three Gorges Reservoir (TGR) in China since 2003, many of which were slow-moving landslides. Normally, these landslides do not occur suddenly, but accelerations during short periods may occur, and they can still cause damage to buildings. The initiation of slow-moving landslides depends not only on the hydraulic characteristics of the sliding body, but also on the mechanical properties of the sliding zone soil. In this study, the Sifangbei landslide in the TGR was selected as a case study to analyse the residual strength of the sliding zone and the long-term monitoring data. The analysis of the long-term monitoring data indicated that the periodic landslide deformation reactivation was affected by seasonal rainfall and annual reservoir water-level fluctuations. Soil samples along the sliding zone collected from the front and middle of the landslide were tested using a ring shear test to study the influence of the water content and shear rate on the residual strength values. The results showed that an increase in water content can weaken the shear strength of sandy clay and clay. The sandy clay with fewer montmorillonite minerals and more sand particles had higher mechanical properties than the clay with more montmorillonite minerals and fewer sand particles. The damaging effect of the increased water content on the sandy clay was mostly reflected in the residual friction angle, while the damaging effect of the increased water content on the clay was mostly reflected in the residual cohesion. An increase in the shear rate had a positive effect on the shear strength of the sandy clay and clay. For the sandy clay with high particle friction, the shear mode changed from turbulent flow to slippage as large particles on the sliding surface broke into smaller particles when the shear rate reached a high speed ( v ≥ 0.5 mm min −1 ). The shear strength under different scenarios revealed the mechanism of slow-moving landslide reactivation.

A large number of high voltage transmission tower foundations crossing mountainous and hilly areas are often located in high-prone slope areas of landslide disasters. Applying appropriate protective measures to improve their stability is the key to ensuring the continuous and safe operation of transmission lines. To study the protection effect of different protection measures on the tower foundation landslide, this paper takes the Yanzi landslide in Badong County, Hubei Province as a geological prototype, designs and produces a physical test model, and carries out physical model tests of the landslide under extreme rainfall conditions(50, 100 mm/h) without protection, applying anti-slide piles and lattice protection. The deformation and failure characteristics of the landslide and the protective effect of different protective measures are revealed from the experimental point of view. The results show that under two extreme conditions, the unprotected landslide experienced the evolution process of slope surface erosion, crack propagation, local collapse and deformation, and overall sliding. The anti-slide pile measures have a significant effect on the overall protection of the landslide. The landslide is in a stable state, the deformation of the tower foundation is small, and the inclination rate of the tower meets the specification, but the slope surface will be scoured and collapsed. Lattice slope protection measures can effectively reduce the risk of slope erosion and slope toe collapse, but the overall stabilization of the tower foundation under continuous heavy rainfall is slightly weaker. The model test results are consistent with the historical deformation of the landslide and the actual treatment effect. The test conclusions can provide a reference for the failure mechanism research and protection engineering design of similar tower foundation landslides.

危险性评价是区域滑坡灾害风险评价及风险管控工作中的关键内容.以三峡库区重庆市云阳县为例,首先利用逐步判别法从初始指标体系中筛选关键指标体系(坡度、高程、剖面曲率、地形湿度指数、岩土体类型、植被覆盖度和距道路距离),并基于逻辑回归模型完成全区滑坡易发性区划;然后以距水系5 km范围将研究区划分为非库岸区和库岸区,对非库岸区考虑不同降雨重现期下的滑坡时间概率,对库岸区考虑不同库水位状态与不同降雨重现期下的滑坡时间概率;最后综合时间概率和易发性结果得到云阳县区域滑坡灾害危险性区划图.研究结果表明:区内滑坡主要发育在强降雨+低库水位状态工况下;云阳县滑坡较高危险区及高危险区占研究区总面积的52.7％,主要沿长江干流及其支流水系展布,分布在城镇周边等人类工程活动开发程度较大的建设用地区域.研究成果对于提高危险性评价结果的精度和发展滑坡灾害风险评价理论具有借鉴价值.

依据历史降雨数据与滑坡事件的相关性,准确地确定滑坡临界降雨预警阈值,是区域滑坡灾害防治工作的关键问题.选取湖南张家界市为典型研究区,采用I-D模型,分别考虑区内降雨型滑坡的破坏模式、斜坡结构、坡体形态、滑体面积、厚度及至坡度的差异,系统开展不同发育特征滑坡的精细化预警阈值分析.结果 表明:研究区推移式滑坡临界雨量阈值要小于牵引式滑坡;顺向坡、斜向坡、横向坡和逆向坡滑坡的临界降雨阈值逐渐增大;凸型、平直型、阶型和凹型斜坡孕育滑坡的临界降雨阈值依次增加;滑坡面积和厚度与降雨阈值呈现正相关关系,较大规模滑坡的发生需要较大的临界降雨值;在25°-35°之间对应的降雨阈值最低.研究结果可为类似地区降雨型滑坡的精细化预警预报提供科学依据和借鉴.

危岩体多孕育于高陡岩质斜坡之上,具有明显的隐蔽性和突发性,其早期的快速识别与稳定性评价一直是地质灾害防治工作中重要的技术难题之一.以重庆万州狮子头为例,利用小型无人机合理规划航线获取高清影像,生成二三维模型.通过遥感信息提取技术与地质灾害分析相结合,为危岩体稳定性研究提供基础数据.运用图像识别技术识别危岩体结构面,并利用前期提取的坐标等基础数据计算出其产状.最后采用赤平投影的方法分析了典型危岩单体的稳定性.研究成果为高位危岩体的非接触式测量、精细地质信息获取及稳定性快速评价提供一种新的思路.