Search Results (80)

This research paper presents an assessment of geohazards on Lefkas Island, Greece, using Geographic Information System (GIS) technology to map risk and enhance public awareness through an interactive web application. Natural hazards such as landslides, floods, wildfires, and desertification threaten both the safety of residents and the island’s tourism-dependent economy, particularly due to its seismic activity and Mediterranean climate. By combining the Sendai Framework for Disaster Risk Reduction with GIS capabilities, we created detailed hazard maps that visually represent areas of susceptibility and provide critical insights for local authorities and the public. The web application developed serves as a user-friendly platform for disseminating hazard information and educational resources, thus promoting community preparedness and resilience. The findings highlight the necessity for proactive land management strategies and community engagement in disaster risk reduction efforts. This study underscores GIS’s pivotal role in fostering informed decision making and enhancing the safety of Lefkas Island’s inhabitants and visitors in the face of environmental challenges. Full article

This study presents a digital twin–based framework for assessing rockfall hazards at the immediate vicinity of the Rumkale Archaeological Site, a geologically sensitive and culturally significant location in southeastern Türkiye. Historically associated with early Christianity and strategically located along the Euphrates, Rumkale is a protected heritage site that attracts increasing numbers of visitors. Here, high-resolution photogrammetric models were generated using imagery acquired from a remotely piloted aircraft system and post-processed with ground control points to produce a spatially accurate 3D digital twin. Field-based geomechanical measurements including discontinuity orientations, joint classifications, and strength parameters were integrated with digital analyses to identify and evaluate hazardous rock blocks. Kinematic assessments conducted in the study revealed susceptibility to planar, wedge, and toppling failures. The results showed the role of lithological structure, active tectonics, and environmental factors in driving slope instability. The proposed methodology demonstrates effective use of digital twin technologies in conjunction with traditional geotechnical techniques, offering a replicable and non-invasive approach for site-scale hazard evaluation and conservation planning in heritage contexts. This work contributes to the advancement of interdisciplinary methods for geohazard-informed management of cultural landscapes. Full article

Sarez Lake, situated at one of the highest altitudes among naturally dammed lakes, is regarded as potentially hazardous due to its geological setting. Therefore, developing an integrated monitoring and risk assessment framework for slope-related geological hazards in this region holds significant scientific and practical value. In this study, we processed 220 Sentinel-1A SAR images acquired between 12 March 2017 and 2 August 2024, using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to extract time-series deformation data with millimeter-level precision. These deformation measurements were combined with key environmental factors to construct a susceptibility evaluation model based on the Information Value and Support Vector Machine (IV-SVM) methods. The results revealed a distinct spatial deformation pattern, characterized by greater activity in the western region than in the east. The maximum deformation rate along the shoreline increased from 280 mm/yr to 480 mm/yr, with a marked acceleration observed between 2022 and 2023. Geohazard susceptibility in the Sarez Lake area exhibits a stepped gradient: the proportion of area classified as extremely high susceptibility is 15.26%, decreasing to 29.05% for extremely low susceptibility; meanwhile, the density of recorded hazard sites declines from 0.1798 to 0.0050 events per km². The spatial configuration is characterized by high susceptibility on both flanks, a central low, and convergence of hazardous zones at the front and distal ends with a central expansion. These findings suggest that mitigation efforts should prioritize the detailed monitoring and remediation of steep lakeside slopes and fault-associated fracture zones. This study provides a robust scientific and technical foundation for the emergency warning and disaster management of high-altitude barrier lakes, which is applicable even in data-limited contexts. Full article

Data bias in geohazard artificial intelligence (AI) systems, particularly class distribution imbalances, critically undermines the reliability of landslide detection models. While active learning (AL) offers promise for mitigating annotation costs and addressing data biases, the interplay between landslide class proportions and AL efficiency remains poorly quantified; additionally, self-optimizing mechanisms to adaptively manage class imbalances are underexplored. This study bridges these gaps by rigorously evaluating how landslide-to-non-landslide ratios (1:1, 1:12, and 1:30) influence the effectiveness of a widely used AL strategy—margin sampling. Leveraging open-source landslide inventories, we benchmark margin sampling against random sampling using the area under the receiver operating characteristic curve (AUROC) and partial AUROC while analyzing spatial detection accuracy through classification maps. The results reveal that margin sampling significantly outperforms random sampling under severe class imbalances (1:30), achieving 12–18% higher AUROC scores and reducing false negatives in critical landslide zones. In balanced scenarios (1:1), both strategies yield comparable numerical metrics; however, margin sampling produces spatially coherent detections with fewer fragmented errors. These findings indicate that regardless of the landslide proportion, AL enhances the generalizability of landslide detection models in terms of predictive accuracy and spatial consistency. This work also provides actionable guidelines for deploying adaptive AI systems in data-scarce, imbalance-prone environments. Full article

In early 2025, the Santorini–Amorgos area (Aegean Volcanic Arc, Greece) experienced a seismic swarm, with dozens of M ≥ 4.0 earthquakes and a maximum magnitude of M = 5.2. Beyond its seismological interest, the sequence was notable for triggering rare increased preparedness actions by Greek Civil Protection operational structures in anticipation of an imminent destructive earthquake. These actions included (i) risk communication, (ii) the reinforcement of operational structures with additional personnel and equipment on the affected islands, (iii) updates to local emergency plans, (iv) the dissemination of self-protection guidance, (v) the activation of emergency alert systems, and (vi) volunteer mobilization, including first aid and mental health first aid courses. Although it was in line with contingency plans, public participation was limited. Volunteers helped bridge this gap, focusing on vulnerable groups. The implemented actions in Greece are also compared with increased preparedness during the 2024–2025 seismic swarms in Ethiopia, as well as preparedness before the highly anticipated major earthquake in Istanbul (Turkey). In Greece and Turkey, legal and technical frameworks enabled swift institutional responses. In contrast, Ethiopia highlighted the risks of limited preparedness and the need to embed disaster risk reduction in national development strategies. All cases affirm that preparedness, through infrastructure, planning, communication, and community engagement, is vital to reducing earthquake impacts. Full article

Landslide is one of the most frequent and destructive geohazards around the world. The accurate identification of potential landslides plays a vital role in the management of landslide risk. The use of unmanned aerial vehicle (UAV) techniques has recently gained much popularity in landslide assessment; however, most of the current UAV-image-based landslide identifications rely upon visual inspections. In this paper, an image-analysis-based landslide identification framework is developed to detect the landslides in UAV images by recognizing the landslide boundaries and ground surface cracks. In this framework, object-oriented image analysis is undertaken to identify the potential landslide boundaries in the input UAV images and the ground surface cracks in the UAV images are recognized by an automatic ground surface crack recognition model, which is trained through a deep transfer learning strategy. With the aid of this transfer learning strategy, the crack recognition model trained can take advantage of the feature of local ground surface cracks in the concerned area and the crack recognition model that has well been developed based on the samples of ground surface cracks collected from different landslide sites. Then, the landslide boundaries and the ground surface cracks obtained are fused based on Boolean operations; the fusion results can allow for informed landslide identification in UAV Images. To illustrate the effectiveness of the proposed image-analysis-based landslide identification framework, the Heifangtai Terrace of Gansu, China, was selected as a study area, and the identification results are further validated through comparisons with the field survey results. Full article

This study explores the growing application of 3D remote sensing in geohazard studies, particularly for rock slope monitoring. It highlights the use of cost-effective Street View Imagery (SVI) and Unmanned Aerial Vehicles (UAV) through Structure-from-Motion (SfM) photogrammetry as tools for 3D rockfall monitoring. Using multi-temporal SVI and UAV Imagery from the Koto Panjang cliff in Indonesia, we quantify rockfall volume changes over seven years and assess associated geohazards. The results reveal a total rockfall retreat of 5270 m³, with an average annual rate of 7.53 m³/year. Structural analysis identified six major discontinuity sets and confirmed inherent instability within the rock mass. Kinematic simulations using SVI and UAV-derived data further assessed rockfall trajectories and potential impact zones. Results indicate that 40% of simulated rockfall deposits accumulated near existing roads, with significant differences in distribution based on scree slope angles. This emphasizes the role of scree slope in influencing rockfall propagation. In conclusion, SVI and UAV imagery presents a valuable tool for 3D point cloud reconstruction and rockfall hazard assessment, particularly in areas lacking historical data. The study showcases the effectiveness of using SVI and UAV imagery in quantifying historical past rockfall volume and identifies critical areas for mitigation strategies, highlighting the importance of scree slope angle in managing rockfall hazard. Full article

Understanding the relationship between geological and geomorphological processes is essential for reconstructing landscape evolution. This study examines how geology and geomorphology shape landscape development in central Brazil, focusing on the Natividade Group area. Sentinel-2 and SRTM data were integrated with geospatial analyses to produce two key maps: (i) a pedo-geomorphological map, classifying landforms and soil–landscape relationships, and (ii) a predictive geological–geomorphological map, based on a machine learning-based prediction of geomorphic units, which employed a Random Forest classifier trained with 15 environmental predictors from remote sensing datasets. The predictive model classified the landscape into six classes, revealing the ongoing interactions between geology, geomorphology, and surface processes. The pedo-geomorphological map identified nine pedoforms, grouped into three slope classes, each reflecting distinct lithology–relief–soil relationships. Resistant lithologies, such as quartzite-rich metasedimentary rocks, are associated with shallow, poorly developed soils, particularly in the Natividade Group. In contrast, phyllite, schist, and Paleoproterozoic basement rocks from the Almas and Aurumina Terranes support deeper, more weathered soils. These findings highlight soil formation as a critical indicator of landscape evolution in tropical climates. Although the model captured geological and geomorphological patterns, its moderate accuracy suggests that incorporating geophysical data could enhance the results. The landscape bears the imprint of several tectonic events, including the Rhyacian amalgamation (~2.2 Ga), Statherian taphrogenesis (~1.6 Ga), Neoproterozoic orogeny (~600 Ma), and the development of the Sanfranciscana Basin (~100 Ma). The results confirm that the interplay between geology and geomorphology significantly influences landscape evolution, though other factors, such as climate and vegetation, also play crucial roles in landscape development. Overall, the integration of remote sensing, geospatial analysis, and machine learning offers a robust framework for interpreting landscape evolution. These insights are valuable for applications in land-use planning, environmental management, and geohazard assessment in geologically complex regions. Full article

Tunnels beneath reservoirs are prone to significant geohazards, such as water and mud surges during excavation. To mitigate construction risks during the excavation of the Dajianshan Tunnel, a three-dimensional refined numerical model was developed. This study employed a fluid–solid coupling numerical model to analyze the temporal and spatial variations of the filtration field during the excavation and drainage of the tunnel section beneath the reservoir, and to assess its impact on pore pressure at the reservoir bottom. The results indicate that excavation and drainage initially cause a rapid decrease in pore water pressure at the tunnel vault, which gradually stabilizes. Furthermore, the extent of disturbance in the surrounding rock’s filtration field increases with distance from the tunnel vault. When the excavation intersects fault zones, water surges significantly affect filtration conditions at the reservoir bottom, resulting in a pore pressure reduction of approximately 5.2 kPa. Additionally, under blasting disturbance conditions, a larger disturbance range and higher permeability in the loosened zone led to greater pore pressure fluctuations, posing increased challenges for excavation safety and drainage management. This study provides a predictive model and methodology to prevent construction accidents during tunnel excavation, offering valuable insights for ensuring safety during the construction process. 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

Aba Tibetan and Qiang Autonomous Prefecture (Aba Prefecture), located in Southwest China, has complex geological conditions and frequent seismic activity, facing an increasing landslide risk that threatens the safety of local communities. This study aims to improve the regional geohazard database by identifying slow-moving landslides in the area. We combined Stacking Interferometric Synthetic Aperture Radar (Stacking-InSAR) technology for deformation detection, optical satellite imagery for landslide boundary mapping, and field investigations for validation. A total of 474 slow-moving landslides were identified, covering an area of 149.84 km², with landslides predominantly concentrated in the river valleys of the southern and southeastern regions. The distribution of these landslides is strongly influenced by bedrock lithology, fault distribution, topographic features, proximity to rivers, and folds. Additionally, 236 previously unknown landslides were detected and incorporated into the local geohazard database. This study provides important scientific support for landslide risk management, infrastructure planning, and mitigation strategies in Aba Prefecture, offering valuable insights for disaster response and prevention efforts. Full article

In early 2024, the largest full-scale exercise (FSE) for earthquakes and related geohazards in Greece was implemented in Crete Island (southern Greece). Crete is characterized by intense seismicity with historical and recent destructive earthquakes with considerable impact on both the natural and built environment and subsequently on the population. The uniqueness of this FSE lies in the creation and coordination of a multi-agency, multijurisdictional, and multidisciplinary environment in which a multitude of central, regional, and local stakeholders and a large percentage of the total population of Crete actively participated. This paper constitutes a descriptive study focusing on the main steps of the exercise management cycle comprising planning, implementation, and evaluation of the FSE. Furthermore, emphasis is given on its purpose and objectives, its main events and subsequent incidents, the participants and their roles, as well as the material developed and distributed to the participants. Through this study, the implemented actions for increasing preparedness of the Civil Protection mechanism in case of earthquakes and related geohazards are highlighted aiming to inform the scientific community and operational staff and to contribute to the seismic risk reduction of regions worldwide with similar seismotectonic and demographic characteristics with Crete. Furthermore, suggestions are made for the integration of multi-hazard episodes in the FSE scenario in order that the Civil Protection authorities will be prepared to handle the synergy of hazards of different types that may arise during a post-earthquake period that create compounding challenges during the emergency response and further increase recovery time. Full article

Landslides are a major global threat, endangering lives, infrastructure, and economies. This paper introduces the Geohazard plugin, an open-source tool for QGIS, designed to support medium–small-scale landslide analysis and management. The plugin integrates several algorithms, including the Groundmotion–C index for evaluating SAR data reliability, Landslide–Shalstab for assessing shallow landslide susceptibility, and Rockfall–Droka for estimating rockfall invasion areas and the rockfall relative (spatial) hazard. An application example is provided for each module to facilitate validation and discussion. A case study from the Western Italian Alps highlights the practical application of the Rockfall–Droka modules, showcasing their potential to identify critical zones by integrating the results on affected areas, process intensity, and preferential paths. Emphasis is given to the calibration of model parameters, a critical aspect of the analysis, achieved through a back-analysis of a rockfall event that occurred in June 2024. The Geohazard plugin streamlines geohazard assessments, providing land managers with actionable insights for decision-making and risk mitigation strategies. This user-friendly GIS tool contributes to enhancing resilience in landslide-prone regions. Full article

This study aims to identify the key factors contributing to the destabilization of the geohazard disaster-pregnant environment in the Yellow River–Huangshui River Valley and provide a robust scientific basis for proactive disaster prevention, management of disaster chains, and mitigation of multi-hazard clusters in unstable regions. The research focuses on the Yellow River–Huangshui River Valley, evaluating the stability of its geohazard disaster-pregnant environment. The disaster-pregnant environment is classified into static and dynamic categories. The static disaster-pregnant environment encompasses factors such as lithology, fracture density, topography, slope, river network density, and vegetation cover. The dynamic disaster-pregnant environment incorporates variables such as extreme rainfall, consecutive rainy days, annual rainfall averages, monthly high temperatures, monthly maximum temperature variations, average annual air temperatures, and human activities. A random forest model was employed to quantitatively assess the stability of the geohazard disaster-pregnant environment in the Yellow River–Huangshui River Valley. The findings indicated that (1) extreme indicators were the primary contributors to the destabilization of the disaster-pregnant environment, with very heavy rainfall contributing 28% and consecutive rainy days contributing 27%. Human activities ranked next, accounting for 15%. (2) Unstable regions for static, dynamic, and integrated disaster-pregnant environments accounted for 44%, 45%, and 44% of the study area, respectively, with all unstable areas concentrated in river valley regions. (3) The overall trend of stability in the disaster-pregnant environment was characterized by widespread instability. Extremely unstable areas were predominantly located in river valley regions, largely influenced by human activities. Conversely, only 0.1% of the region exhibited signs of stability, and 2.1% showed a tendency toward extreme stability. Full article

Geological mapping provides vital information about the structure, evolution, natural resource potential, and geohazards of a specific area. The role of geological mapping is especially valuable for mountainous countries like Georgia. In this context, we present a geological map of the Mulkhura River Valley in the Georgian Caucasus (43°3′ N, 42°52′ E) with accompanying cross-sections at a scale of 1:30,000, covering approximately 220 km². The geological information in the map is based on a comprehensive review of previously published geological maps and literature, combined with original analysis of satellite imagery and hitherto unpublished field data. The extensive spatial coverage and accompanying cross-sections provide detailed insights into the structure of the region. This new map will serve as a foundation for future geological research, hazard management, and resource exploration in the area, as well as for geoconservation to develop the national geotourism industry in this region. Full article