Search Results (44)

To address the limitations of low model accuracy and single-scenario settings in traditional rainfall-induced landslide hazard assessments, this study focuses on Yongren County, Yunnan Province—a region where landslides pose significant threats to sustainable socio-economic development and infrastructure resilience. Eight controlling factors—lithology, slope, terrain relief, distances to faults, rivers, and roads, vegetation coverage, and elevation—were used to build a landslide susceptibility index system. Three internationally recognized machine learning models, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were applied for comparison. The XGBoost model was further coupled with rainfall scenario analysis, simulating three rainfall scenarios—normal, 10-year, and 20-year return periods—to form a framework integrating “high-precision susceptibility prediction–multi-scenario rainfall driving–dynamic hazard assessment.” Results show that XGBoost achieved the highest accuracy and stability, with AUC and overall accuracy exceeding those of RF and SVM, supporting high-precision multi-scenario simulations. High-hazard zones expanded from road-disturbed areas under normal rainfall to riverbanks under 10-year rainfall and to fault-fracture and road–river interaction zones under 20-year rainfall. This study provides a transferable framework for sustainable landslide risk management, enabling precision prevention, optimizing resource allocation for disaster risk reduction, and supporting evidence-based policy-making for sustainable development and climate adaptation in similar geological settings. Full article

This study establishes an integrated “Detection–Decomposition–Interpretation” framework for geohazard assessment, with Tangshan City serving as a representative case. Using Sentinel-1 SAR images from 2020 to 2024, regional surface deformation was derived via the Small Baseline Subset InSAR (SBAS-InSAR) technique. Six categories of geohazards were systematically identified and classified: landslides, open-pit slope deformation, mining-induced subsidence, spoil heap deformation, tailings pond deformation, and reclamation settlement. A total of 115 potential hazards were spatially cataloged, revealing distinct zonation characteristics: the northern mountainous area is predominantly affected by landslides and open-pit mining hazards; the central plain exhibits concentrated mining subsidence; and the southern coastal zone is marked by large-scale reclamation settlement. For the southern reclamation area, where settlement mechanisms are complex, the Independent Component Analysis (ICA) method was applied to successfully decompose the deformation signals into three independent components: IC1, representing the dominant long-term irreversible settlement driven by fill consolidation, building loads, and groundwater extraction; IC2, reflecting seasonal deformation coupled with groundwater level fluctuations; and IC3, comprising residual noise. Time series analysis further reveals the coexistence of “decelerating” and “accelerating” settlement trends across different zones, indicative of their respective evolutionary stages—from decaying to actively progressing settlement. This study not only offers a scientific basis for geohazard prevention and control in Tangshan, but also provides a transferable framework for analyzing hazard mechanisms in other complex geographic settings. Full article

This study investigates co-seismic landslides triggered by the 1 January 2024 Mw 7.6 Noto Peninsula earthquake in Japan using LiDAR differentiation and a modified Savage–Hutter model. By analyzing pre- and post-earthquake high-resolution topographic data from 13 landslides in a geologically homogeneous area of the peninsula, we characterized distinct landslide morphologies and dynamic behaviours. Our approach combined static morphological analysis from LiDAR data with simulations of granular flow mechanics to evaluate landslide mobility. Results revealed two distinct landslide types: those with clear erosion-deposition zonation and complex landslides with discontinuous topographic changes. Landslide dimensions followed power-law relationships (H = 7.51L^0.50, R² = 0.765), while simulations demonstrated that internal deformation capability (represented by the μ parameter) significantly influenced runout distances for landslides terminating on low-angle surfaces but had minimal impact on slope-confined movements. These findings highlight the importance of integrating both static topographic parameters and dynamic flow mechanics when assessing co-seismic landslide hazards, particularly for predicting potential runout distances on gentle slopes where human settlements are often located. Our methodology provides a framework for improved landslide susceptibility assessment and disaster risk reduction in seismically active regions. Full article

Field observations provide valuable information for rockfall assessments, but estimating physical and statistical quantities related to rockfall propagation directly is challenging. Simulations are commonly used to infer these quantities, but their subjectivity can result in varying hazard land use zonation extents for different projects. This paper focuses on the application of simulated trajectories for rockfall hazard assessments, with an emphasis on reducing subjectivity. A quantitative guiding rockfall hazard methodology based on earlier concepts is presented and put in the context of legislated requirements. It details how the temporal hazard component, related to the likelihood of failure, can be distributed spatially using simulated trajectories. The method can be applied with results from any process-based software and combined with various prediction methods of the temporal aspect, although this aspect is not the primary focus. Applied examples for static objects and moving objects, such as houses and vehicles, are shown to illustrate the important effect of the object size. For that purpose, the methodology was applied at an indicative level over Norway utilizing its 1 m detailed digital terrain model (DTM) acquired from airborne LiDAR. Potential rockfall sources were distributed in 3D where slopes are steeper than 50°, as most rockfall events in the national landslide database (NSDB) occurred in such areas. This threshold considerably shifts toward gentler slopes when repeating the analysis with coarser DTMs. Simulated trajectories were produced with an adapted version of the simulation model stnParabel. Comparing the number of trajectories reaching the road network to the numerous related registered rockfall events of the NSDB, an indicative averaged yearly frequency of released rock fragments of 1/25 per 10,000 m² of cliff was obtained for Norway. This average frequency can serve as a starting point for hazard assessments and should be adjusted to better match local conditions. Full article

The available historical documents for the city of Alexandria indicate that it was damaged to varying degrees by several (historical and instrumentally recorded) earthquakes and by highly destructive tsunamis reported at some places along the Mediterranean coast. In this work, we applied the neo-deterministic seismic hazard analysis (NDSHA) approach to the Alexandria metropolitan area, estimating ground motion intensity parameters, e.g., peak ground displacement (PGD), peak ground velocity (PGV), peak ground acceleration (PGA), and spectral response, at selected rock sites. The results of this NDSHA zonation at a subregional/urban scale, which can be directly used as seismic input for engineering analysis, indicate a relatively high seismic hazard in the Alexandria region (e.g., 0.15 g), and they can provide an essential knowledge base for detailed and comprehensive seismic microzonation studies at an urban scale. Additionally, we established detailed tsunami hazard inundation maps for Alexandria Governorate based on empirical relations and considering various Manning’s Roughness Coefficients. Across all the considered scenarios, the average estimated time of arrival (ETA) of tsunami waves for Alexandria was 75–80 min. According to this study, the most affected sites in Alexandria are those belonging to the districts of Al Gomrok and Al Montazah. The west of the city, called Al Sahel Al Shamally, is less affected than the east, as it is protected by a carbonate ridge parallel to the coastline. Finally, we emphasize the direct applicability of our study to urban planning and risk management in Alexandria. Our study can contribute to identifying vulnerable areas, prioritizing mitigation measures, informing land-use planning and building codes, and enhancing multi-hazard risk analysis and early warning systems. Full article

Landslides pose a significant threat worldwide, leading to numerous fatalities and severe economic losses. The city of Manizales, located in the Colombian Andes, is particularly vulnerable due to its steep topography and permeable volcanic ash-derived soils. This study aims to assess landslide hazards in Manizales by integrating shallow planar and deep-seated circular failure mechanisms using physics-based models (TRIGRS and Scoops3D). By combining hazard zonation maps with rainfall thresholds calibrated through historical data, we provide a refined approach for early warning systems (EWS) in the region. Our results underscore the significance of the landslide hazard maps, which combine shallow planar and deep-seated circular failure scenarios. By categorizing urban areas into high, medium, and low-risk zones, we offer a practical framework for urban planning. Moreover, we developed physics-based rainfall thresholds for early landslide warning, simplifying their application while aiming to enhance regional predictive accuracy. This comprehensive approach equips local authorities with essential tools to mitigate landslide risks, refine hazard zoning, and strengthen early warning systems, promoting safer urban development in the Andean region and beyond, as the physics-based methods used are well-established and implemented globally. Full article

Risk assessment provides a powerful tool for the early warning and risk mitigation of geohazards. However, few efforts have been made regarding risk assessment and dynamic control at multiple scales. With respect to this issue, the West Han River catchment in the Gansu Province of China was taken as a study area, and geohazard risk assessments at three different scales were carried out, namely regional, local and site scales. Hazard assessment was performed using the combination of the information value and hierarchical analysis models, infinite slope stability model, and FLO-2D model. Vulnerability was estimated from two viewpoints, including physical vulnerability and social vulnerability, by applying remote sensing and semi-quantitative methods. Finally, risk mapping and zonation was obtained from the products of hazard and vulnerability, and corresponding measures of risk management and control at different scales were recommended. The results indicated that the geohazard risk at the regional scale was the highest under the earthquake and rainfall conditions with a 100-year (100a) return period, respectively, and the area of very high risk level reached 5%. When the rainfall condition had a return period of 50 years, only 1% of the area was located in the very high-risk region. Additionally, the overall risk was higher in the central and northeastern parts of the region under heavy rainfall and earthquake conditions. The overall risk level in Longlin-Leiba Town (at the local scale) responded more significantly to heavy rainfall conditions, with higher risk in the southwestern, central, and northeastern parts of the region. For the site scale (Wujiagou debris flow), only 2% of the total area was identified as very high-risk even under heavy rainfall with a 100a return period, but the proportions for the low and moderate levels reached 30% and 56%, respectively. The present study can provide scientific references for geohazard risk assessment and control. Full article

The geological and topographic conditions in the upper reaches of the Jinsha River are intricate, with frequent occurrences of landslides. Landslide Susceptibility Prediction (LSP) in this area is a crucial aspect of geological disaster risk management. This study constructs an LSP model using a Convolutional Neural Network (CNN) based on a Bilateral Aggregation Guidance (BAG) strategy, termed BGA-Net. A comprehensive landslide hazard analysis, integrating static landslide susceptibility zonation with dynamic surface deformation monitoring, was therefore conducted. The study area selected was the upper reaches of the Jinsha River, particularly the site of the Baige landslide. The BGA-Net model was first proposed for LSP generation, achieving an accuracy exceeding 85%, while the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology was jointly applied to comprehensively analyze the dynamic geological hazard risk at a regional scale. The final results were presented in a lookup table format and mapped to delineate and grade each risk level. The results show the method is practical, with high feasibility. Compared with traditional machine learning methods, the BGA-strategy-oriented CNN model more effectively differentiated the extremely low- and extremely high-susceptibility areas, enhancing decision-making processes. Full article

Shear wave velocity (V_s) is a critical parameter in geophysical investigations, micro-zonation research, and site classification. In instances where conducting direct tests at specific locations is challenging due to equipment unavailability, limited space, or initial instrumentation costs, it becomes essential to estimate V_s directly, using empirical correlations for effective site characterization. The present review paper explores the correlations of V_s with the standard penetration test (SPT) for geotechnical site characterization. V_s, a critical parameter in geotechnical and seismic engineering, is integral to a wide range of projects, including foundation design and seismic hazard assessment. The current paper provides a detailed analysis of the key findings, implications for geotechnical engineering practice, and future research needs in this area. It emphasizes the importance of site-specific calibration, the impact of geological background, depth-dependent behavior, data quality control, and the integration of V_s data with other geophysical methods. The review underlines the continuous monitoring of V_s values due to potential changes over time. Addressing these insights and gaps in research contributes to the accuracy and safety of geotechnical projects, particularly in seismic-prone regions. Full article

To prevent large-scale water inrush accidents during the excavation process of a water-rich tunnel, a method, based on a random forest (RF) algorithm, for predicting the hazard level of water inrush is proposed. By analyzing hydrogeological conditions, six factors were selected as evaluating indicators, including stratigraphic lithology, inadequate geology, rock dip angle, negative terrain area ratio, surrounding rock grade, and hydrodynamic zonation. Through the statistical analysis of 232 accident sections, a dataset of water inrush accidents in water-rich tunnels was established. We preprocessed the dataset by detecting and replacing outliers, supplementing missing values, and standardizing the data. Using the RF model in machine learning, an intelligent prediction model for the hazard of water inrush in water-rich tunnels was established through the application of datasets and parameter optimization processing. At the same time, a support vector machine (SVM) model was selected for comparison and verification, and the prediction accuracy of the RF model reached 98%, which is higher than the 87% of the SVM. Finally, the model was validated by taking the water inrush accident in the Yuanliangshan tunnel as an example, and the predicted results have a high degree of consistency with the actual hazard level. This indicates that the RF model has good performance when predicting water inrush in water-rich tunnels and that it can provide a new means by which to predict the hazard of water inrush in water-rich tunnels. Full article

Determining the spatio-temporal patterns of rockfalls, such as the zonation of hazards and the assessment of associated risks, can be challenging due to poor historical archives. Dendrogeomorphological methods cover this lack of data and provide reliable reconstructions of rockfall activities over several centuries. These methods are based on the signals recorded in the tree rings that are affected by the mechanical impact of falling rock fragments. In this study, we analyzed the spatial and temporal distribution of rockfalls in a 0.19 ha forest area in the Southern Carpathians. We collected 170 samples (100 increment cores and 70 stem discs) from all 40 Picea abies (L.) Karst trees identified in the study area (1 tree/47 m²). This allowed us to date 945 events between 1817 and 2021, which we then compared with available weather records. Our results show the main trajectory of falling rock fragments from the source area, as well as significant temporal variations in process activity. These variations correlate only slightly with fluctuations in meteorological parameters. Despite the expected intensification of natural hazards due to climate warming, our study area shows a general trend towards a slight decrease in rockfall activity at present. Full article

The accurate prediction of noise levels at outdoor locations requires detailed data of the noise sources and terrain parameters and an efficient model for prediction. However, the possibility of predicting noise with reasonable accuracy using less input data is a challenge and needs to be studied scientifically. The qualities of the noise data, terrain parameters, and prediction model can impact the accuracy of the prediction significantly. This study primarily focuses on the dependency of noise data for efficient noise prediction and mapping. This research article proposes a detailed methodology to predict and map the noise and exposure levels in Ratapur, Uttar Pradesh, India, with various granularities of noise data inputs. The noise levels were measured at various places and at different times of the day at 10 min intervals. Different data input proportions and qualities were used for noise prediction, namely, (1) a large data-based method, (2) a small data-based method, (3) a source point average data-based method, (4) a Google navigation data-based method, and (5) accurate modelling using an ANN-based method, integrating accurate noise data with a sophisticated modelling algorithm for noise prediction. The analysis of the variation between the predicted and measured noise levels was conducted for all five of the methods using the ANOVA technique. Various methods based on less noise data methods predicted the noise levels with accuracies within the ±4–10 dB(A) range, while the ANN-based technique predicted it with an accuracy of ±0.5–2.5 dB(A). Interestingly, the estimation of the noise exposure levels (>85 dB(A)) and the identification of hazard zones around the studied road intersection could also be performed efficiently even when using the data-deficient models. This paper also showcased the possibility of predicting an accurate 3D map for an area by extracting vehicles and terrain features from satellite images without any direct recording of noise data. This paper thus demonstrated approaches to reduce the noise data dependency for noise prediction and mapping and to enable accurate noise-hazard zonation mapping. Full article

Landslides are the most common natural hazard in the Piemonte region (northwestern Italy). This study is focused on shallow landslides caused by the sliding of the surficial detrital-colluvial cover caused by rainfall and characterized by a sudden and fast evolution. This study investigates shallow landslide events compared with variables considered as main predisposing qualitative factors (lithology, pedology and land use) to obtain a zonation of shallow landslide susceptibility in a GIS environment. Additionally, wildfire occurrence is also evaluated as a further predisposing factor for shallow landslide initiation. The resulting susceptibility map shows a strong correlation between the first three variables and shallow landslide occurrence, while it shows a negligible, or very localized, relationship with wildfire occurrence. Through the intersection of the predisposing factors with the landslide data points, a map of homogeneous zones is obtained; each identified zone is characterized by uniform lithological, soil-type, and land-use characteristics. The shallow landslide density occurrence is computed for each zone, resulting in a four-range susceptibility map. The resulting susceptibility zones can be used to define and evaluate the hazard linked to shallow landslide events for civil protection and regional planning purposes. Full article

With the large-scale mining of deeply buried coal seams, the risk of roof water inrush increases during mining. In order to ensure safe mining, it is necessary to predict the risk potential of water inrush from the roof aquifer. This study introduces a coupling evaluation method, including the analytic hierarchy process (AHP), principal component analysis (PCA), and improved Game theory (IGT). This paper takes the water inrush from the roof aquifer of the 11-2 coal seam in Kouzidong mine as the research object. An evaluation index system is constructed by selecting six evaluation factors, including the aquitard effective thickness, aquiclude thickness, the ratio of sandstone to mudstone, rock quality designation, fault fractal dimension, and wash water quantity of geological log. The comprehensive weighting method based on IGT is used to optimize the subjective and objective weighting values obtained by AHP and PCA methods in turn, and an AHP–PCA–IGT evaluation model is established to divide and evaluate the water inrush risk zonation of the roof aquifer. The risk degree of the water inrush gradually decreases from the center to the north–south, and the main areas with relatively high risks and higher risks are distributed in a small part of the western and eastern regions. Finally, combining various drilling data examples, drilling pumping tests, and water inrush sites, the accuracy of the predicted results is validated through the vulnerability fitting percentage (VFP). The predictions are basically consistent with the actual results, and this study lays a theoretical foundation for the prevention and control of water inrush hazards. Full article

The Indian Himalayan region is experiencing frequent hazards and disasters related to permafrost. However, research on permafrost in this region has received very little or no attention. Therefore, it is important to have knowledge about the spatial distribution and state of permafrost in the Indian Himalayas. Modern remote sensing techniques, with the help of a geographic information system (GIS), can assess permafrost at high altitudes, largely over inaccessible mountainous terrains in the Himalayas. To assess the spatial distribution of permafrost in the Alaknanda Valley of the Chamoli district of Uttarakhand state, 198 rock glaciers were mapped (183 active and 15 relict) using high-resolution satellite data available in the Google Earth database. A logistic regression model (LRM) was used to identify a relationship between the presence of permafrost at the rock glacier sites and the predictor variables, i.e., the mean annual air temperature (MAAT), the potential incoming solar radiation (PISR) during the snow-free months, and the aspect near the margins of rock glaciers. Two other LRMs were also developed using moderate-resolution imaging spectroradiometer (MODIS)-derived land surface temperature (LST) and snow cover products. The MAAT-based model produced the best results, with a classification accuracy of 92.4%, followed by the snow-cover-based model (91.9%), with the LST-based model being the least accurate (82.4%). All three models were developed to compare their accuracy in predicting permafrost distribution. The results from the MAAT-based model were validated with the global permafrost zonation index (PZI) map, which showed no significant differences. However, the predicted model exhibited an underestimation of the area underlain by permafrost in the region compared to the PZI. Identifying the spatial distribution of permafrost will help us to better understand the impact of climate change on permafrost and its related hazards and provide necessary information to decision makers to mitigate permafrost-related disasters in the high mountain regions. Full article