Geological Hazards Research Articles

Optimizing mountain railway alignments considering geo-hazard impacts based on a knowledge graph

Abstract Alignment design is pivotal in determining the macroscopic hazard to a railway, especially in complex mountainous regions with dense geologic hazards. In this work, to apply the experience of handling geo-hazards that accumulated in manual alignment design, the related alignment design principles are summarized via an event tree. Based on this, a method is proposed to construct a knowledge graph (called GeRAD-KG) for mountain railway alignment design considering geo-hazard impacts of debris flows, landslides and rockfalls, with which to structurally represent corresponding design principles. Then, a construction cost—geologic hazard bi-objective optimization model incorporating the knowledge-driven hazard assessment is constructed. To solve the model, a GeRAD-KG-guided distance transform algorithm integrating a multicriteria tournament decision is tailored. Finally, a real-world case indicates that the alternative generated by the proposed method can reduce the construction cost and geological hazard compared to the single-objective optimization alternative, which verifies the effectiveness of proposed method for assisting actual railway design in a mountainous region with dense geo-hazards.

Development of Highway Construction Route Selection Based on Ecological Sensitivity Evaluation and Intervention Optimization Strategy Research

Ecological sensitivity is an essential indicator for measuring the degree of ecological fragility of a region, and traditional highway routing ignores the ecological benefits of regional nature, which causes irreversible impacts on the natural environment. Based on the AHP method, this paper carries out a comprehensive assessment of the ecological sensitivity of the K0+000~K56+438 section of the Guanzhuang Tourist Highway in the Zhangjiajie Wuling Mountain Area from 2000 to 2020, and it utilizes the spatial autocorrelation analysis method to reveal the pattern of its spatial and temporal changes in ecological sensitivity. The following results were obtained. (1) The ecological condition of the study area is gradually improving, and the areas with higher sensitivity are mainly distributed in the northeast and southwest of the study area. (2) The ecological sensitivity of the study area has strong spatial autocorrelation, but the autocorrelation has been reduced in recent years, and the distribution of the ecological sensitivity has been gradually discretized. (3) There are problems of high ecological risk and construction difficulty in the traditional highway route selection scheme of the A line. The optimized route selection scheme, the K line, can effectively avoid highly sensitive areas by adjusting the route and bridge settings. Finally, the article proposes engineering construction suitability and ecological restoration strategies for different road sections regarding vegetation cover, landscape risk, and geological hazards. This study establishes a set of comprehensive technical chains for tourism highway routing in ecologically fragile areas based on the spatial and temporal evolution of sensitivity, which provides new ideas for traditional highway routing and is of great significance for maintaining the balance and stability of ecosystems within the road area.

Spatiotemporal Variations and Driving Factors of Ecological Sensitivity in the West Qinling Mountains, China, Under the Optimal Scale

This study selected the five indicators of soil erosion, climate environment, geological hazards, biodiversity, and human disturbances and uses the entropy weight method to calculate the ecological sensitivity of the West Qinling Mountains from 2000 to 2020. The analysis produced a spatiotemporal distribution of ecological sensitivity over the 20-year period. An equal step size of 500 m was used to progressively increase the spatial scale from 500 m to 6 km. The optimal scale for the spatial differentiation of ecological sensitivity in the West Qinling Mountains was determined by analyzing the characteristics of changes at different scales, response mechanisms, and optimal parameters for geographical detector spatial scale identification. Based on this scale, the change in intensity and pattern and the influencing factors of ecological sensitivity were analyzed. The results show the following: (1) The 5.5 km spatial scale balances the requirements of accuracy, spatial heterogeneity, and data adequacy, making it the optimal scale for analyzing the spatiotemporal variation patterns of ecological sensitivity in the West Qinling Mountains. (2) From 2000 to 2020, the mean ecological sensitivity in the West Qinling Mountains exhibited a decreasing trend, indicating an improvement in the ecological environment. Spatially, the ecological sensitivity of the West Qinling Mountains showed a spatial distribution pattern of “low in the west and high in the east, low in the south and high in the north”. During the study period, the ecological sensitivity in the West Qinling region remained generally stable, with no high-frequency changes observed. (3) Population density is the primary driving factor of spatial differentiation of ecological sensitivity in the West Qinling Mountains, while GDP serves as a secondary factor. Overall, socioeconomic factors have the most significant impact on ecological sensitivity. (4) Over 75% of the ecological sensitivity trends exhibit patterns of perennial unchanged and fluctuating unchanged trends, with areas of fluctuating increase smaller than areas of fluctuating decrease. Regions of perennial high sensitivity are primarily concentrated in the northeastern part of the West Qinling Mountains, while areas with increased fluctuation in ecological sensitivity are mainly located in the western and southern parts of the West Qinling Mountains. Future efforts should focus on these regions.

Geological hazards on the territory of Russia: their distribution and development prediction

Geological hazards on the territory of Russia: their distribution and development prediction

Land Subsidence Predictions Based on a Multi-Component Temporal Convolutional Gated Recurrent Unit Model in Kunming City

Land subsidence (LS) is a geological hazard driven by both natural conditions and human activities. Traditional LS time-series prediction models often struggle to accurately capture nonlinear data characteristics, leading to suboptimal predictions. To address this issue, this paper introduces a multi-component temporal convolutional gate recurrent unit (MC-TCGRU) model, which integrates a fully adaptive noise-ensemble empirical-mode decomposition algorithm with a deep neural network to account for the complexity of time-series data. The model was validated using typical InSAR subsidence data from Kunming, analyzing the impact of each component on the prediction performance. A comparative analysis with the TCGRU model and models based on seasonal-trend decomposition using LOESS (STL) and empirical-mode decomposition (EMD) revealed that the MC-TCGRU model significantly enhanced the prediction accuracy by reducing the complexity of the original data. The model achieved R² values of 0.90, 0.93, 0.51, 0.93, and 0.96 across five points, outperforming the compared models.

Displacement field monitoring of tunnel faces using terrestrial laser scanning data

The instability of tunnel faces is a serious geological hazard, thus requiring monitoring and prediction of the stability of tunnel faces. This paper presents a high-accuracy method for estimating the displacement fields of tunnel faces from terrestrial laser scanning data. The proposed method consists of four parts: i) the development of a stable rigid-body reference under planar strain assumption, ii) the fine alignment of the rigid-body references using the developed local deformation constrained rigid-body assumption (LDC-RBA) and local cross-validation iterative closest point (LCV-ICP) algorithm, iii) the segmentation of tunnel faces using a machine learning classifier, and iv) tracking of tunnel face displacement vectors under the LDC-RBA. Qualitative experimental results demonstrate that the estimated displacement fields of the tunnel face using the proposed method conform to the deformation law. Furthermore, quantitative experimental results indicate that the estimation accuracy of tunnel face displacement fields achieves millimeter-level.

Diurnal Cycles of Cloud Properties and Precipitation Patterns over the Northeastern Tibetan Plateau During Summer

In the context of rising temperatures and increasing humidity in Northwest China, substantial gaps remain in understanding the mechanisms of land–atmosphere cloud–precipitation coupling across the northeastern Tibetan Plateau (TP), Loess Plateau (LP), and Huangshui Valley (HV). This study addresses these gaps by investigating cloud properties and precipitation patterns utilizing the Fengyun-4 Satellite Quantitative Precipitation Estimation Product (FY4A-QPE) and ERA5 datasets. We specifically focus on Lanzhou, a pivotal city within the LP, and Xining, which epitomizes the HV. Our findings reveal that diurnal variations in precipitation are significantly less pronounced in the eastern regions compared to northeastern TP. This discrepancy is attributed to marked diurnal fluctuations in convective available potential energy (CAPE) and wind shear between 200 and 500 hPa. While both cities share similar wind shear patterns and moisture transport directions, Xining benefits from enhanced snowmelt and effective water retention in surrounding mountains, resulting in higher precipitation levels. Conversely, Lanzhou suffers from moisture deficits, with dry, hot winds exacerbating the situation. Notably, precipitation in Xining is strongly correlated with CAPE, influenced by diurnal variability, and intensified by valley and lake–land breezes, which drive afternoon convection. In contrast, Lanzhou’s precipitation exhibits a weak relationship with CAPE, as even elevated values fail to generate significant cloud formation due to insufficient moisture. The ongoing trends of warming and humidification may lead to improved precipitation patterns, especially in the HV, with potential ecological benefits. However, concentrated rainfall during summer afternoons and midnights raises concerns regarding extreme weather events, highlighting the susceptibility of the HV to geological hazards. This research underscores the need to further explore the uncertainties inherent in precipitation dynamics in these regions.

A Case Study for Analysis of Stability and Treatment Measures of a Landslide Under Rainfall with the Changes in Pore Water Pressure

Mining causes damage to the soil and rock mass, while rainfall has a pivotal impact on the mining slope stability, even leading to geological hazards such as landslides. Therefore, the study evaluated the mine landslide stability and determined the effectiveness of the treatment measures under the impact of pore water pressure changes caused by rainfall, taking the Kong Mountain landslide in Nanjing, Jiangsu Province, China, as the research object. The geological conditions and deformation characteristics were clarified, and the failure mechanism and influencing factors were analyzed. Also, the landslide stability was comprehensively evaluated and calculated utilizing the finite element-improved limit equilibrium method and FLAC 3D 6.0, which simulated the distribution of pore water pressure, displacement, etc., to analyze the influence of rainfall conditions and reinforcement effects. The results indicated the following: (1) Rainfall is the key influencing factor of the landslide stability, which caused the pore water pressure changes and the loosening of the soil due to the strong permeability; (2) The distribution of the pore water pressure and plastic zone showed that, during the rainfall process, a large area of transient saturation zone appeared at the leading edge, affecting the stability of the whole landslide and led to the further deformation; (3) After the application of treatment measures (anti-sliding piles and anchor cables), the landslide stability increased under both natural and rainfall conditions (from 1.02 and 0.94 to 1.38 and 1.31, respectively), along with a reduction in displacement, plastic zones, etc. The Kong Mountain landslide, with the implemented treatment measures, is in good stability, which is in line with the evaluation and calculation results. The study provides certain contributions to the stability evaluation and treatment selection of similar engineering under rainfall infiltration.

Examining the contribution of lithology and precipitation to the performance of earthquake-induced landslide hazard prediction

Earthquake-induced landslides (EQIL) are one of the most catastrophic geological hazards. Immediate and swift evaluation of EQIL hazard in the aftermath of an earthquake is critically important and of substantial practical value for disaster reduction. The selection of influencing factor layers is crucial when using machine learning methods to predict EQIL hazard. As important input factors for EQIL hazard models, lithology and precipitation are extensively employed in forecasting EQIL hazard. However, few work explored whether these layers can improve the accuracy of EQIL hazard predictions. With Random Forest (RF) models, we employed a traditional and a state-of-the-art sampling strategy to assess EQIL modelling with and without lithology and precipitation data for the 2022 Luding earthquake in China. First, by excluding both factors, we used eight other influencing factors (land use, slope aspect, slope, elevation, distance to faults, distance to rivers, NDVI, and peak ground acceleration) to generate a landslide hazard map. Second, lithology and precipitation were separately added to the original EQIL hazard models. The results indicate that neither lithology nor precipitation have positive effects on the prediction of EQIL for both sampling strategies. The high-risk areas (or low-risk areas) tend to cluster within certain lithology types or precipitation ranges, which significantly affects the accuracy of the hazard map. Additionally, the model with the state-of-the-art sampling strategy deteriorates more than the model with the traditional sampling strategy. We believe this is very likely due to the strong spatial clustering of negative sample points caused by the latest sampling strategy. Our findings will contribute to the assessment of post-earthquake landslide hazards and the advancement of emergency disaster mitigation efforts.

Advancements in Technologies and Methodologies of Machine Learning in Landslide Susceptibility Research: Current Trends and Future Directions

Landslides are pervasive geological hazards that pose significant risks to human life, property, and the environment. Understanding landslide susceptibility is crucial for predicting and mitigating these disasters. This article advocates for a comprehensive review by systematically compiling and analyzing 146 relevant studies up to 2024. It assesses current progress and limitations and offers guidance for future research. This paper provides a comprehensive overview of the diverse challenges encountered by machine learning models in landslide susceptibility assessment, encompassing aspects such as model selection, the formulation of evaluation index systems, model interpretability, and spatial heterogeneity. The construction of an evaluation index system, which serves as the foundational data for the model, profoundly influences its accuracy. This study extensively investigates the selection of evaluation factors and the identification of positive and negative samples, proposing valuable methodologies. Furthermore, this paper briefly deliberates and compares classical machine learning models, offering valuable insights for model selection. Additionally, it delves into discussions concerning model interpretability and spatial heterogeneity issues. These research findings promise to enhance the precision of landslide susceptibility assessments and furnish effective strategies for risk management.

Identifying Potential Landslides in Low-Coherence Areas Using SBAS-InSAR: A Case Study of Ninghai County, China

The southeastern coastal regions of China are characterized by typical hilly terrain with abundant rainfall throughout the year, leading to frequent geological hazards. To investigate the measurement accuracy of surface deformation and the effectiveness of error correction methods using the small baselines subset–interferometry synthetic aperture radar (SBAS-InSAR) method in identifying potential geological hazards in such areas, this study processes and analyzes 129 SAR images covering Ninghai County, China. By processing coherence coefficients using the Stacking technique, errors introduced by low-coherence images during phase unwrapping are mitigated. Subsequently, interferograms with high coherence are selected for time-series deformation analysis based on the statistical parameters of coherence coefficients. The results indicate that, after mitigating errors from low-coherence images, applying the SBAS-InSAR method to only high-coherence SAR datasets provides reliable surface deformation results. Additionally, when combined with field geological survey data, this method successfully identified landslide boundaries and potential landslides not accurately detected in previous geological surveys. This study demonstrates that using the SBAS-InSAR method and selecting high-coherence SAR images based on interferogram coherence statistical parameters significantly improves measurement accuracy and effectively identifies potential geological hazards.

Numerical simulation of deposit-landslide instability induced by water level decrease: a case study of RS deposit in Lancang River

Changes in reservoir water level often trigger landslides along the reservoir banks, which are common geological hazards that significantly affect the construction and operation of hydropower projects. In this paper, the stability of the RS reservoir landslide in the southwestern region of China is investigated. A discrete element seepage analysis model is constructed based on the theory of changes in the infiltration surface of the deposit caused by water level drawdown. The model is then embedded into a continuous-discontinuous three-dimensional numerical simulation method to systematically investigate the influence of reservoir water level decrease on the stability of the RS deposit. The results indicate that, under the condition of a sudden drop in reservoir water level decrease, the deposit experiences destabilization failure. Its sliding velocities are mainly influenced by seepage forces and gravity, while sliding displacements are influenced by seepage forces and topographic conditions. The landslide process shows distinct stratification characteristics. The portion of the deposit that slides into the river channel is mainly distributed between an altitude of 2655 m and 2870 m. The research results provide a scientific basis for studying the mechanism of slope failure and landslide disaster chain under the condition of reservoir water level lowering.

Artificial Neural Networks and Ensemble Learning for Enhanced Liquefaction Prediction in Smart Cities

This paper examines how smart cities can address land subsidence and liquefaction in the context of rapid urbanization in Japan. Since the 1960s, liquefaction has been an important topic in geotechnical engineering, and extensive efforts have been made to evaluate soil resistance to liquefaction. Currently, there is a lack of machine learning applications in smart cities that specifically target geological hazards. This study aims to develop a high-performance prediction model for estimating the depth of the bearing layer, thereby improving the accuracy of geotechnical investigations. The model was developed using actual survey data from 433 points in Setagaya-ku, Tokyo, by applying two machine learning techniques: artificial neural networks (ANNs) and bagging. The results indicate that machine learning offers significant advantages in predicting the depth of the bearing layer. Furthermore, the prediction performance of ensemble learning improved by about 20% compared to ANNs. Both interdisciplinary approaches contribute to risk prediction and mitigation, thereby promoting sustainable urban development and underscoring the potential of future smart cities.

Extreme Precipitation Over the Southern Slope of the Tibetan Plateau and the Associated Atmospheric Circulation Anomalies

AbstractThe southern slope of the Tibetan Plateau (SSTP) is one of the rainiest regions in the world where geological hazards caused by extreme precipitation often occur. This study investigates the characteristics and mechanisms of extreme precipitation over SSTP from June to September during 2001–2020 using Global Precipitation Measurement satellite observation. The extreme precipitation days are defined as the days with top 5% of regional‐mean daily precipitation over SSTP in this period, which has an average precipitation of 27.2 mm/d. Averaging over the extreme precipitation days, precipitation peaks at an altitude of about 300 m, coinciding with the climatological maximum precipitation, but with a much larger value of 37.2 mm/d than the climatology of 11.9 mm/d. Composite analysis of circulations on extreme days reveals significant circulation anomalies in both the lower and upper troposphere. Specifically, the lower‐tropospheric circulations are characterized by significant westerly anomalies over northern India, and the upper‐tropospheric circulations are characterized by northerly anomalies over the central Tibetan Plateau, which are statistically independent. The lower‐tropospheric westerly anomalies blowing toward SSTP are blocked by the topography, favoring extreme precipitation over SSTP. The upper‐tropospheric northerly anomalies, on the other hand, correspond to anomalous northeasterlies north of SSTP in the middle troposphere and southeasterlies to the south in the lower troposphere, and the convergence of these circulation anomalies favors extreme precipitation over SSTP. Lastly, the lower‐tropospheric westerly and upper‐tropospheric northerly anomalies, respectively, correspond to less precipitation over the South Asian monsoon region and the Tibetan Plateau.

Evaluation of the water weakening coefficient of sandstones by using non-destructive physical parameters

IntroductionThe presence of water significantly reduces the mechanical strength of rocks and induces various engineering geological hazards. The water weakening coefficient Kp is used to quantify this effect, defined as the ratio of wet uniaxial compressive strength to the dry value.MethodsA comprehensive physico-mechanical test was conducted on fifteen sandstones under dry and saturated conditions to predict the water weakening coefficient using easily obtainable physical parameters. Multiple linear regression was employed to establish the relationship between these parameters and the saturated water weakening coefficient.ResultsThe saturated water weakening coefficient decreases with increasing porosity and increases with higher Primary wave velocity (P-wave velocity). Rocks with higher porosity but lower P-wave velocity typically absorb more water. The P-wave velocity and clay mineral content were identified as the best predictors of the saturated water weakening coefficient (R2 = 0.82). Unsaturated water weakening coefficients at any water saturation level were well estimated using a previous exponential function.DiscussionThe roles of different clay minerals and P-wave velocity in the water weakening process of rocks are comprehensively discussed. This study enhances the understanding of the water weakening mechanism and provides an improved evaluation model for the water weakening coefficient of sandstones using physical parameters.

Unsupervised Change Detection Methods Applied to Landslide Mapping: Case Study in São Sebastião, Brazil

ABSTRACTLandslides represent a growing global geological hazard, further intensified by climate‐induced changes. Remote sensing data, through its capacity for repetitive collection and change detection techniques, that compare and quantify the spatio‐temporal alterations over time, plays a critical role in landslide detection. Considering the February 2023 São Sebastião event and Sentinel‐2 imagery, we assessed diverse unsupervised change detection techniques, encompassing both traditional and recent machine learning‐based approaches. Notably, the Floating References (FR) and Homogeneous Blocks Single‐class Classification (HBSC) methods outperform classic approaches and deliver the most accurate results with F1‐Score and kappa coefficient exceeding 0.96 and 0.92, respectively. These outcomes demonstrate the efficacy of machine learning in automating landslide delineation and underscore the necessity of meticulous data and parameter selection in achieving high‐accuracy automatic landslide mapping. Lastly, this study fills a significant gap in the existing literature by evaluating unsupervised change detection methods for landslide mapping within the Brazilian context.

Intelligent detection of underground openings and surrounding disturbed zones

This study aims to develop an intelligent method to detect subsurface anomalies for prediction and mitigation of geologic risks. We trained a convolutional neural network (CNN) model using the seismic data from 6 field cases of regular pipes and irregular cavities and a sliding time window technique to augment the datasets, and tested the CNN model using another 2 field cases. We derived probability distribution as an indicator of anomaly existence and validated high-value and low-value probability distributions corresponding to underground openings and surrounding disturbed zones, respectively. We found that the characteristics of subsurface anomalies determines the effective seismic features and influences the CNN model performance. Finally, we applied the CNN model to investigate a circular-bored tunnel and surrounding disturbed zones. Our results demonstrate that the CNN model is fast and accurate to detect the horizontal locations of subsurface anomalies, while the accuracy of vertical locations depends on the estimation of P-wave velocity. The intelligent method has the potential to identify hidden risks at early stages and to mitigate subsequent geologic hazards.

Aerial imagery for geological hazard management in Alpine catchments

Aerial imagery for geological hazard management in Alpine catchments

Geological hazards when justifying the regulations of urban development

Relevance. Territorial planning of cities determines the reduction or prevention of possible or existing losses of the population, economic facilities and the environment from technoprirodnye hazards of different genesis. The effectiveness of planning regulations for urban development depends on the degree of validity of identification and forecasts of the occurrence and evolution of hazardous natural and technogenic processes in time and space. This, in its turn, is determined by the reliability of our ideas about the patterns of their formation and regional distribution. Aim. To identify geological hazards and propose the main engineering and geological constraints in urban planning on the example of Gomel. Objects. Natural and technical system "geological environment – technogenic impacts – hazardous natural and technoprirodnye processes". Methods. Systematic approach to the analysis of patterns of formation of hazardous natural and technoprirodic processes in the city; numerical modeling of geofiltration and geomigration processes based on the geofiltration model "GOMEL". Results. The most characteristic dangerous natural and technoprirodnye processes have been established on the example of the city of Gomel. Priority areas for the introduction of planning restrictions: tectonic situation; man-made flooding, were identified. Soil conditions are considered separately as one of the factors effecting the precipitation of natural and artificial bases under the influence of loads from civil and industrial buildings and structures. The authors have revealed the presence of disjunctive, as well as plicative in the form of flexures, age-varying and multi-scale dislocations forming the block structure of the upper part of the earth's crust of the city. It is shown that from the point of view of the stability of engineering structures, the absolute values of the velocities of long-period unidirectional block displacements are dangerous during long-term operation of structures located in interblock active zones. At the same time, short-period multidirectional movements in the active geodynamic zones of the articulation of blocks cause a change in the slope and bending of the foundations of structures. The paper introduces the engineering-geological features of the most common surface deposits: the terrigenous gray-colored formation of the Paleogene; glacial, periglacial and extraglacial formations of anthropogenic origin and their facies-genetic complexes. The authors identified the potential natural and technological hazards associated with the marked deposits. The paper demonstrates the causes and mechanisms of the formation of technogenic flooding of the city, as well as the genesis of the decrease in the deformation properties of moraine sandy loam with a technogenic increase in humidity. It is concluded that when establishing land use regulations for a city, as a rule, general measures cannot be taken, both for its entire territory and for certain stages of construction activity. The paper introduces the corresponding restrictions on the use of subsoil.

A Study on the Implementation Path of the Blended Teaching Model in Geological Hazards Investigation and Evaluation

Geological Hazards Investigation and Evaluation is the core course of Environmental Geological Engineering, aiming to cultivate skilled talents with solid theoretical knowledge and excellent practical skills. At present, the course faces several issues, including a teaching environment disconnected from real-world work scenarios, course content that deviates from job-related tasks, a lack of digital teaching resources, and reliance on a single teaching method, leading to students’ poor feedback from employers. Based on the concept of outcome-based education, the course team of Geological Hazards Investigation and Evaluation establishes a “five-step double-rotation” blended teaching model with the help of a Small Private Online Course platform. The program is designed to improve the teaching environment and expand the digitalized teaching resources in order to improve students’ learning motivation, enhance learning effectiveness, and cultivate skillful talents who meet employers’ satisfaction.