Search Results (18)

Understanding the dynamic response and failure mechanisms of rock slopes during earthquakes is crucial in sustainable geohazard prevention and mitigation engineering. The initiation of landslides involves complex interactions between seismic wave propagation, dynamic rock mass behavior, and crack network evolution, and these interactions are heavily influenced by the slope geometry, lithology, and structural parameters of the slope. However, systematic studies remain limited due to experimental challenges and the inherent variability of landslide scenarios. This study employs Discrete Element Method (DEM) modeling to comprehensively investigate how geological structure parameters control the dynamic amplification and deformation characteristic of typical bedding/anti-dip layered slopes consist of parallel distributed rock masses and joint faces, with calibrated mechanical properties. A soft-bond model (SBM) is utilized to accurately simulate the quasi-brittle rock behavior. Numerical results reveal distinct dynamic responses between bedding and anti-dip slopes, where local amplification zones (LAZs) act as seismic energy concentrators, while potential sliding zones (PSZs) exhibit hindering effects. Parametric analyses of strata dip angles and thicknesses identify a critical dip range where slope stability drastically decreases, highlighting high-risk configurations for earthquake-induced landslides. By linking the slope failure mechanism to seismic risk reduction strategies, this work provides practical guidelines for sustainable slope design and landslide mitigation in tectonically active regions. Full article

Trail erosion is a global issue, particularly in mountainous regions, that is largely driven by increased tourist flows and uncontrolled trampling. Our study was conducted in the Bucegi Mountains, Southern Carpathians, Romania, along three of the most frequented hiking trails, each with varying levels of difficulty. Two of these trails cross both the forest and alpine zones, and the other crosses only the alpine zone: Jepii Mici, connecting the Bușteni resort (960 m a.s.l.) to Babele Chalet (2200 m a.s.l.); Jepii Mari, linking Bușteni resort to the National Sports Complex Piatra Arsă (1960 m a.s.l.); and the trail between Babele Chalet and Omu Peak (2505 m a.s.l.). Our analysis focused on morphometric parameters, the volume of displaced soil, and associated geohazards, serving as indicators for assessing the degradation state of hiking trails and their suitability for mountain biking and tourist traffic. The findings reveal a high degree of trail degradation, highlighted by increased trail width, the development of parallel trail sections due to dispersed tourist traffic, areas with abrupt gradient changes, and sections severely affected by erosion, resulting in significant volumes of displaced soil. These factors hinder effective tourist traffic, including hiking and mountain biking, and degrade the mountainous landscape. Conversely, the results can be useful for both monitoring annual trail erosion rates and facilitating tourist access, tailored to individual and group interests, as well as the physical readiness of each tourist, to offer a more pleasurable and sustainable experience. Full article

Shear wave velocity (V_s) is a key geomechanical variable in subsurface exploration, essential for hydrocarbon reservoirs, geothermal reserves, aquifers, and emerging use cases, like carbon capture and storage (CCS), offshore geohazard assessment, and deep Earth exploration. Despite its broad significance, no comprehensive multidisciplinary review has evaluated the latest applications, estimation methods, and challenges in V_s prediction. This study provides a critical review of these aspects, focusing on energy-efficient prediction techniques, including geophysical surveys, remote sensing, and artificial intelligence (AI). AI-driven models, particularly machine learning (ML) and deep learning (DL), have demonstrated superior accuracy by capturing complex subsurface relationships and integrating diverse datasets. While AI offers automation and reduces reliance on extensive field data, challenges remain, including data availability, model interpretability, and generalization across geological settings. Findings indicate that integrating AI with geophysical and remote sensing methods has the potential to enhance V_s prediction, providing a cost-effective and sustainable alternative to conventional approaches. Additionally, key challenges in V_s estimation are identified, with recommendations for future research. This review offers valuable insights for geoscientists and engineers in petroleum engineering, mining, geophysics, geology, hydrogeology, and geotechnics. Full article

The Henan section of the Yellow River Basin (3.62 × 10⁴ km², 21.7% of Henan Province), a vital agro-industrial and politico-economic hub, faces frequent rainfall-induced geohazards. The 2021 “7·20” Zhengzhou disaster, causing 398 fatalities and CNY 120.06 billion loss, highlights its vulnerability to extreme weather. While machine learning (ML) aids geohazard assessment, rainfall-induced geological hazard susceptibility assessment (RGHSA) remains understudied, with single ML models lacking interpretability and precision for complex disaster data. This study presents a hybrid framework (IVM-ML) that integrates the Information Value Model (IVM) and ML. The framework uses historical disaster data and 11 factors (e.g., rainfall erosivity, relief amplitude) to calculate information values and construct a machine learning prediction model with these quantitative results. By combining IVM’s spatial analysis with ML’s predictive power, it addresses the limitations of conventional single models. ROC curve validation shows the Random Forest (RF) model in IVM-ML achieves the highest accuracy (AUC = 0.9599), outperforming standalone IVM (AUC = 0.7624). All models exhibit AUC values exceeding 0.75, demonstrating strong capability in capturing rainfall–hazard relationships and reliable predictive performance. Findings support RGHSA practices in the mid-Yellow River urban cluster, offering insights for sustainable risk management, land-use planning, and climate resilience. Bridging geoscience and data-driven methods, this study advances global sustainability goals for disaster reduction and environmental security in vulnerable riverine regions. Full article

Urban development and climate change are two main impacting factors in the thermal environment of cities. This study aims to analyze future changes in Mean Radiant Temperature (MRT), one of the main contributors to human thermal comfort and the concept of Urban Heat Island (UHI), considering climate change and urban development scenarios in the study area, Fredericton, New Brunswick, by 2050. The analysis utilizes the SOLWEIG (Solar and Longwave Environmental Irradiance Geometry) model from the Urban Multi-scale Environmental Predictor (UMEP) platform to calculate MRT values. By integrating these two impacting factors, this research provides insights into the potential future changes in MRT levels and the resulting thermal conditions and geohazards in the study area. The analysis enables the identification of areas susceptible to increased radiant heat exchange due to the proposed changes in land cover, urban morphology, and air temperature. Furthermore, this study contributes to a better understanding of the complex interactions between climate change, urbanization, and urban microclimates. By incorporating MRT assessments and prioritizing thermal comfort, cities can develop strategies to mitigate the negative effects of UHI and create sustainable and livable urban environments for future generations. Full article

There has been an increase in the frequency of hazards associated with meteorological and hydrological phenomena. One of them is flash floods occurring episodically in areas of concentrated runoff—valleys without permanent drainage. In the opinion of residents and local authorities, these are potentially safe areas—they are not threatened by floods and are therefore often occupied by buildings. The importance of addressing flash floods in land use planning is essential for sustainable development and disaster risk reduction. The objective of this research was to assess the level of the hazard and to evaluate its presence in land use planning activities. This manuscript fills a research gap, as to date flash flood threats have not been analyzed for individual buildings located in catchments of dry valleys in temperate climates. More than 12,000 first-order catchments were analyzed. The study covered an upland area located in East Poland, which is characterized by high population density and dispersed rural settlement. Within the 10 municipalities, buildings located on potential episodic runoff lines were identified. Qualitative assessment was applied to ascertain the susceptibility of catchments to flash floods. Such criteria as slopes, size, shape of the catchment area, and land cover, among others, were used. Between 10 and 20% of the buildings were located on episodic runoff lines, and about 900 sub-catchments were highly or very highly susceptible to flash floods. The way to reduce the negative effects of these phenomena is to undertake proper land use planning based on knowledge of geohazards, including flash floods. However, an analysis of available planning documents shows that phenomena of this type are not completely taken into account in spatial management processes. Full article

Geohazards pose an essential role to the preservation of cultural and natural heritage areas, given their valuable significance in terms of scenic, natural, and cultural characteristics, forming unique landscapes that require proactive action to achieve sustainable environmental management. To address these challenges, a methodological framework focusing on geohazard prevention, emphasizing the importance of a pre-management stage that enables stakeholders to prioritize resources and implement landscape planning strategies, is proposed in this paper. In this framework, an integrated set of geospatial, geological, meteorological, and other relevant environmental factors to quantify cumulative geohazard zones in heritage areas is utilized. Implementing advanced tools such as geographic information systems (GISs), remote sensing techniques, and geospatial data analysis, a clustering and characterization of various geohazards is obtained, providing a comprehensive understanding of their cumulative impacts. The introduction of a cumulative geohazard index is proposed in this paper to better understand and then assess the impacts of environmental-driven geohazards that may affect cultural and natural heritage areas to be embedded into the impact assessment process. The validation of the proposed geohazard framework and index is performed in the Parrhasian Heritage Park in Peloponnese, Greece. The outcomes of the analysis highlight the need to mitigate geohazard impacts through early and in situ targeted actions to facilitate the decision-making process and contribute to the protection of evolving landscapes with cultural and natural elements for future generations. Full article

Cities all over the world have developed on different geological-geomorphological substrates. Different kinds of human activities have operated for millennia as geomorphic agents, generating numerous and various erosion landforms and huge anthropogenic deposits. Considering the increasing demand for land and the expansion of the built-up areas involving and disturbing any kind of natural system inside and surrounding the actual urban areas, it is not negligible how important the dynamics of the urban environment and its physical evolution are. In this context, this manuscript addresses insights into eight case studies of urban geomorphological analyses of cities in Italy, Greece, and Brazil. The studies are based on surveying and mapping geomorphological processes and landforms in urban areas, supporting both geo-hazard assessment, historical evolution, and paleomorphologies, as well as disseminating knowledge of urban geoheritage and educating about the anthropogenic impact on urban sustainability. We hypothesize that urban geomorphological analysis of several case studies addresses the physical environment of modern cities in a multi-temporal, multidisciplinary, and critical way concerning global changes. Thus, this study aims to illustrate and propose a novel approach to urban geomorphological investigation as a model for the understanding and planning of the physical urban environment on a European and global scale. Full article

The mountainous areas in the western part of Sichuan Province are mostly Alpine Gorge regions with high mountains, steep slopes, complex topography and geomorphology, special climatic conditions, infertile soils, and fragile ecological environments. In this study, a geohazard risk assessment was carried out in the Alpine Gorge region to prevent geohazards from hindering socio-economic development, affecting the lives and safety of residents, and undermining sustainable development in the region. With the help of a geographic information system (GIS), the analysis of geohazard influence factors was carried out; eight indicators, such as elevation and slope aspect, were selected to construct the evaluation index system. Additionally, the time and space distribution pattern of each influence factor and geohazard was analyzed. Geologic hazards in the region are influenced mainly by precipitation and human engineering activities. The prediction and evaluation of geohazard risk in Jiulong County are based on the Information Value model (IV), the Logistic Regression model (LR), and the Random Forest model (RF). Comparing the Receiver operating characteristic (ROC) curves of the three models for the accuracy test, the results show that all three models are suitable for the Alpine Gorge region, and the Logistic Regression model has the highest accuracy. Based on the evaluation results, measures and countermeasures for geologic disaster prevention and mitigation are proposed in light of the reality of geologic disaster prevention and mitigation work in Jiulong County. The research results can guide the government’s disaster prevention and mitigation work, provide a scientific basis for formulating regional geologic disaster prevention and control strategies, and ultimately promote the region’s sustainable development. Full article

Geological hazards significantly threaten the safety of China’s railway network. As the railway system continues to expand, particularly with the effects of accelerated climate change, approximately 70% of the newly encountered geohazards occur outside of known areas. This study proposes a novel approach that can be applied to railway systems to identify potential geohazards, analyze risk areas, and assess section vulnerability. The methodology uses integrated remote sensing technology to effectively enhance potential railway hazard identification timeliness. It combines kernel density, hotspot, and inverse distance-weighted analysis methods to enhance applicability and accuracy in the risk assessment of railway networks. Using a case study in southeastern Gansu as an example, we identified 3976 potential hazards in the study area, analyzed five areas with high concentrations of hazards, and 11 districts and counties prone to disasters that could threaten the railway network. We accurately located 16 sections and 20 significant landslide hazards on eight railway lines that pose operational risks. The effectiveness of the methodology proposed in this paper has been confirmed through field investigations of significant landslide hazards. This study can provide a scientific basis for the sustainability of the railway network and disaster risk management. Full article

The continuous prohibition of commercial logging and intensifying conservation endeavors have encompassed the implementation of the Natural Forest Conservation Program (NFCP) and the Grain-to-Green Program (GTGP) by the Chinese government since 1999. Nevertheless, the efficacy of the commercial logging ban and its effectiveness in halting deforestation remain uncertain. Likewise, the destructive aftermath of the 7.9 magnitude Wenchuan earthquake in 2008 continues to be under scrutiny, necessitating ongoing study and analysis. Thus, there exists a pressing need to comprehensively monitor the spatio-temporal evolution of the forest habitat and assess the ecological status over the past two decades. The Jiudingshan Nature Reserve (JNR) is situated in the upper reaches of the Tuojiang River basin in Sichuan province, China, constituting an integral part of the Giant Panda National Park (GPNP). In this study, we classified land cover types and conducted a meticulous monitoring of forest habitat alterations within JNR, by a multilayer perceptron model (MLP) with a highly learning-sensitive algorithm. To quantify these changes, the Simple Ratio Index (SRI) and the Normalized Difference Vegetation Index (NDVI) were computed from Landsat TM/OLI images of four years (i.e., 1997, 2007, 2008, and 2018). Additionally, elevation, slope, aspect, and other topographic data were acquired from the Digital Elevation Model (DEM). The findings of our study unveil a notable expansion in both the scope and proportion of mixed conifer and broadleaf forest from 1997 to 2004. The growth of coniferous forest and the augmented areas of mixed conifer and broadleaf forest signify a substantial improvement in panda habitat. However, the seismic event of 2008 exhibited a pronounced adverse impact on vegetation, particularly within forested regions. Although there is evidence of forest recovery spanning 21 years, concerns regarding fragmentation linger. It is pivotal to acknowledge the potential long-term adverse implications arising from widespread socio-economic development and a multitude of geohazards. Hence, sustained long-term monitoring coupled with effective management strategies remain pivotal for the preservation and rehabilitation of the Giant Panda National Park (GPNP) and giant panda habitat in the future. Full article

Riverside landslides present a significant geohazard globally, posing threats to infrastructure and human lives. In line with the United Nations’ Sustainable Development Goals (SDGs), which aim to address global challenges, professionals in the field have developed diverse methodologies to analyze, assess, and predict the occurrence of landslides, including quantitative, qualitative, and semi-quantitative approaches. With the advent of computer programs, quantitative techniques have gained prominence, with computational intelligence and knowledge-based methods like artificial neural networks (ANNs) achieving remarkable success in landslide susceptibility assessments. This article offers a comprehensive review of the literature concerning the utilization of ANNs for landslide susceptibility assessment, focusing specifically on riverside areas, in alignment with the SDGs. Through a systematic search and analysis of various references, it has become evident that ANNs have emerged as the preferred method for these assessments, surpassing traditional approaches. The application of ANNs aligns with the SDGs, particularly Goal 11: Sustainable Cities and Communities, which emphasizes the importance of inclusive, safe, resilient, and sustainable urban environments. By effectively assessing riverside landslide susceptibility using ANNs, communities can better manage risks and enhance the resilience of cities and communities to geohazards. While the number of ANN-based studies in landslide susceptibility modeling has grown in recent years, the overarching objective remains consistent: researchers strive to develop more accurate and detailed procedures. By leveraging the power of ANNs and incorporating relevant SDGs, this survey focuses on the most commonly employed neural network methods for riverside landslide susceptibility mapping, contributing to the overall SDG agenda of promoting sustainable development, resilience, and disaster risk reduction. Through the integration of ANNs in riverside landslide susceptibility assessments, in line with the SDGs, this review aims to advance our knowledge and understanding of this field. By providing insights into the effectiveness of ANNs and their alignment with the SDGs, this research contributes to the development of improved risk management strategies, sustainable urban planning, and resilient communities in the face of riverside landslides. Full article

Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering potential long-term benefits for sustainable development, remains a challenge for the practitioner and researcher. In this study, for the first time, an automated machine learning (AutoML) approach was proposed for model development and slope stability assessments of circular mode failure. An updated database with 627 cases consisting of the unit weight, cohesion, and friction angle of the slope materials; slope angle and height; pore pressure ratio; and corresponding stability status has been established. The stacked ensemble of the best 1000 models was automatically selected as the top model from 8208 trained models using the H2O-AutoML platform, which requires little expert knowledge or manual tuning. The top-performing model outperformed the traditional manually tuned and metaheuristic-optimized models, with an area under the receiver operating characteristic curve (AUC) of 0.970 and accuracy (ACC) of 0.904 based on the testing dataset and achieving a maximum lift of 2.1. The results clearly indicate that AutoML can provide an effective automated solution for machine learning (ML) model development and slope stability classification of circular mode failure based on extensive combinations of algorithm selection and hyperparameter tuning (CASHs), thereby reducing human efforts in model development. The proposed AutoML approach has the potential for short-term severity mitigation of geohazard and achieving long-term sustainable development goals. Full article

Landslide susceptibility assessment serves as a critical scientific reference for geohazard control, land use, and sustainable development planning. The existing research has not fully considered the potential impact of the spatial agglomeration and dispersion of landslides on assessments. This issue may cause a systematic evaluation bias when the field investigation data are insufficient, which is common due to limited human resources. Accordingly, this paper proposes two novel strategies, including a clustering algorithm and a preprocessing method, for these two ignored features to strengthen assessments, especially in high-susceptibility regions. Multiple machine learning models are compared in a case study of the city of Bijie (Guizhou Province, China). Then we generate the optimal susceptibility map and conduct two experiments to test the validity of the proposed methods. The primary conclusions of this study are as follows: (1) random forest (RF) was superior to other algorithms in the recognition of high-susceptibility areas and the portrayal of local spatial features; (2) the susceptibility map incorporating spatial feature messages showed a noticeable improvement over the spatial distribution and gradual change of susceptibility, as well as the accurate delineation of critical hazardous areas and the interpretation of historical hazards; and (3) the spatial distribution feature had a significant positive effect on modeling, as the accuracy increased by 5% and 10% after including the spatial agglomeration and dispersion consideration in the RF model, respectively. The benefit of the agglomeration is concentrated in high-susceptibility areas, and our work provides insight to improve the assessment accuracy in these areas, which is critical to risk assessment and prevention activities. Full article

Romania is one of the countries severely affected by numerous natural hazards, where landslides constitute a very common geomorphic hazard with strong economic and social impacts. The analyzed area, known as the “Ciuperca Hill”, is located in Oradea (NW part of Romania) and it has experienced a number of landsliding events in previous years, which have endangered anthropogenic systems. Our investigation, focused on the main causal factors, determined that landslide events have rather complex components, reflected in the joint climatological characteristics, properties of the geological substrate, and human activity that further contributed to the intensive change of landscape and acceleration of slope instability. Analysis of daily precipitation displays the occurrence and intensive distribution between May and September. Higher values of rainfall erosivity (observed for the 2014–2017 period), are occurring between April and August. Erosivity density follows this pattern and indicates high intensity events from April until October. SPI index reveals the greater presence of various wet classes during the investigated period. Geological substrate has been found to be highly susceptible to erosion and landsliding when climatological conditions are suitable. Accelerated urbanization and reduced vegetation cover intensified slope instability. The authors implemented adequate remote-sensing techniques in order to monitor and assess the temporal changes in landslide events at local level. Potential solutions for preventative actions are given in order to introduce and conduct qualitative mitigation strategies for shaping sustainable urban environments. Results from this study could have implications for mitigation strategies at national, regional, county, and municipality levels, providing knowledge for the enhancement of geohazard prevention and appropriate response plans. Full article