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A major threat is posed to both human society and the environment by geological hazards, necessitating the need for accurate and comprehensive investigation and application in this field. Traditional geological hazard monitoring technologies are not only inefficient, but also fail to accurately delineate affected areas and provide comprehensive disaster data sets, preventing researchers from conducting precise investigations, detection and prevention. Therefore, this article summarizes the application of oblique photography technology through examples, especially its application in the investigation of local geological disasters such as debris flows and landslides, and the use of orthogonal-based corrected single-view oblique photography technology in the investigation of regional geological disasters such as earthquakes. Through summary, it is found that compared with traditional methods, UAV oblique photography technology is more excellent in the accuracy of investigation, detection, prevention, terrain information identification and geological disaster risk factor analysis in geological disasters. UAV oblique photography technology plays a vital role in the investigation, detection and prevention of geological disasters due to its versatility, efficiency and accuracy. It can help create detailed maps and 3D models of terrain by capturing high-resolution images from multiple angles to help identify potential geohazards. It can also detect changes in landforms, vegetation or waterways to provide warning signs for landslides, sinkholes or provide early warning signals for potential geological hazards such as erosion. High-resolution imagery captured by drones allows rescuers to quickly assess impacts on infrastructure, settlements and natural resources, thereby facilitating the efficient allocation of resources for rescue and recovery efforts. Drone oblique photography technology significantly enhances the ability to investigate, detect and prevent geohazards by providing timely, detailed spatial information critical for informed decision-making and proactive disaster management. Additionally considering that drone oblique photography technology may be combined with artificial intelligence algorithms and virtual reality (VR) imaging technology in the future, this integration is expected to improve the efficiency and accuracy of geological disaster management, thus ensuring the safety of human life and property.

Preliminary tunnel surveys are essential for identifying geological hazards such as aquifers, faults, and karstic zones. While first-arrival tomography is effective for imaging shallow anomalies, traditional seismic sources face significant limitations in forested mountainous regions due to mobility, cost, and environmental impact. To address this, we deployed a seismic source delivered by an unmanned aerial vehicle (UAV) for a highway tunnel survey in Lijiang, China. The UAV system, paired with nodal geophones, enabled rapid, low-impact, and high-resolution data acquisition in rugged terrain. To enhance the weak far-offset refractions affected by near-surface attenuation, we applied supervirtual refraction interferometry (SVI), which significantly improved the signal-to-noise ratio and expanded the usable first-arrival dataset. The combined use of UAV excitation and SVI processing produced a high-precision P-wave velocity model through traveltime tomography, aligned well with borehole data. This model revealed the spatial distribution of weathered zones and bedrock interfaces, and allowed us to infer potential fracture zones. The results offer critical guidance for tunnel alignment and hazard mitigation in complex geological settings.

Xiangyun County is a typical mountainous county. At present, few people have studied geological hazards in Xiangyun County, so it is particularly important to choose appropriate methods for disaster assessment in the study area. (1) Research background: Xiangyun County is located in western Yunnan, China, where geological disasters such as landslides and collapses occur frequently. Not only endanger the safety of people's lives and property, but also destroy the ecological environment within the territory. (2) Methods: The distribution characteristics and influencing factors of geological hazards in Xiangyun County were studied and analyzed, and 9 influencing factors such as elevation, slope, slope direction, stratigraphic lithology, NDVI and rainfall were selected to evaluate the disaster susceptibility of the study area. Through the correlation analysis of evaluation factors, the evaluation system is constructed by combining the information model and the information—logistic regression model. Using ArcGIS software, the study was divided into 5 grades: extremely high, high, medium, low and extremely low prone areas. (3) Results: ROC curve was used to test the evaluation results of the two models respectively. The accuracy of the information model was 74%, and the accuracy of the coupling model was 83%. Extremely high and high-risk areas account for 18% and 24% of the total area, respectively. (4) Conclusion: The results show that the coupled model test has a high precision and can provide a reliable basis for this evaluation.

Geological hazards often occur in mountainous areas and are sudden and hidden, so it is important to identify and assess geological hazards. In this paper, the western mountainous area of Beijing was selected as the study area. We conducted research on landslides, collapses, and unstable slopes in the study area. The surface deformation of the study area was monitored by multi-temporal interferometric synthetic aperture radar (MT-InSAR), using a combination of multi-looking point selection and permanent scatterer (PS) point selection methods. Random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and recurrent neural network (RNN) models were selected for the assessment of geological hazard susceptibility. Sixteen geological hazard-influencing factors were collected, and their information values were calculated using their features. Multicollinearity analysis with the relief-F method was used to calculate the correlation and importance of the factors for factor selection. The results show that the deformation rate along the line-of-sight (LOS) direction is between −44 mm/year and 28 mm/year. A total of 60 geological hazards were identified by combining surface deformation with optical imagery and other data, including 7 collapses, 25 unstable slopes, and 28 landslides. Forty-eight of the identified geological hazards are not recorded in the Beijing geological hazards list. The most effective model in the study area was RF. The percentage of geological hazard susceptibility zoning in the study area is as follows: very low susceptibility 27.40%, low susceptibility 28.06%, moderate susceptibility 21.19%, high susceptibility 13.80%, very high susceptibility 9.57%.

The increasing occurrence of geological hazards along roadway infrastructures presents a significant concern. Evaluating geological hazard susceptibility along roads is a critical aspect of geological disaster emergency response and rescue efforts. Accurate evaluation outcomes are essential as they play a crucial role in mitigating potential financial losses. However, previous studies on geological hazard susceptibility treated all samples as independent entities, overlooking their spatial interactions. This study introduces a novel geological hazard susceptibility assessment model termed the multi-kernel density information (MKDI) method. The MKDI method integrates information value with kernel density estimation, effectively capturing the spatial dependencies among samples. Furthermore, distinct bandwidths are prescribed for various scales of disasters to facilitate multi-kernel density estimation for geological hazards. The integration of the information method enables the development of a comprehensive geological hazard susceptibility map, capturing the spatial complexities of geological hazard distribution. To validate the effectiveness of the proposed method, the study area selected for investigation was the G219 National Highway within Zayu County. Various factors were considered for geological hazard susceptibility mapping, including slope, aspect, profile and plan curvature, river and road linear densities, peak ground acceleration, seismic response spectrum characteristics, lithology, elevation, rainfall, and landform. The results show that the MKDI model outperformed previous methods, achieving an AUC value of 0.99. The derived susceptibility map is expected to offer a scientific basis for urban planning, construction, and geological hazard risk management in the study area.

This study presents an integrated framework that combines spatial clustering techniques and multi-source geospatial data to comprehensively assess and understand geological hazards in Hunan Province, China. The research integrates self-organizing map (SOM) and geo-self-organizing map (Geo-SOM) to explore the relationships between environmental factors and the occurrence of various geological hazards, including landslides, slope failures, collapses, ground subsidence, and debris flows. The key findings reveal that annual average precipitation (Pre), profile curvature (Pro_cur), and slope (Slo) are the primary factors influencing the composite geological hazard index (GI) across the province. Importantly, the relationships between these key factors and GI exhibit spatial variability, as evidenced by the random intercept and slope models, highlighting the need for customized mitigation strategies. Additionally, the study demonstrates that land use patterns and stratigraphic stratum lithology significantly impact the cluster-specific relationships between the key factors and GI, emphasizing the importance of natural resource management for effective geological hazard mitigation. The proposed integrated framework provides valuable insights for policymakers and resource managers to develop spatially-aware strategies for geological hazard risk reduction and climate change adaptation.

Public participation in disaster preparedness and mitigation activities become an important part of disaster risk management. The impact of risk perception and protection motivation theory on preparedness in various types of disasters have been widely reported, and risk attitudes have similarly been studied as a factor influencing preparedness decisions. However, the adaptability of the results on the impact of risk perception on preparedness behavior was questioned in recent years. Especially in the field of geological disasters, very limited studies about the protection motivation theory and risk attitudes have been conducted in China. Therefore, this study designed a questionnaire of people’s risk perceptions and the perception of protective measures based on the protection motivation theory and individual risk attitude factors. The involved field research was conducted in Ganluo County, Liangshan Prefecture, Sichuan Province in 2020. Based on the research data, the structural equation modeling method was used to analyze the effects of threat appraisal, coping appraisal and risk attitudes on people’s willingness to engage in protective behaviors. The results show that individual coping appraisals have a significant effect on the willingness to engage in adaptive behavior, and the risk attitude factor added to the extended model also shows a significant effect on the willingness to prepare for disasters. The results of the study have important practical implications for the encouragement of multilevel participation in risk management in geological hazard-prone areas.

Coal and gas outbursts have been a major geological hazard to underground coal mining for over 150 years and continue to cause serious problems in all over the world. In order to have a better understanding of the phenomenon, it is worthwhile making a historical review of the occurrences and a combat of the events. Many investigations and researches have been done to characterize and prevent the outburst occurrences in the worldwide, but there has been no detailed investigation about coal and gas outburst occurrences in Turkey. This paper presents the outburst data of coal mines in Turkey since 1969. Based on the observation of outburst occurrence in Turkey in the period from 1969 to 2012 as well as mining and geological conditions, detailed analysis of the possible causes of outburst accidents is conducted. The influences of some geological parameters such as the depth of occurrence, thickness and inclination of coal seams, the amount of ejected material (coal and gas), and tectonic disturbances on coal and gas outburst occurrences have been statistically investigated. The outburst occurrences throughout the world were reviewed and compared with the Turkish outburst experiences. Suggestions are put forward on future studies that could be of interest to government agencies regarding strategic policies, proper technical management practice, identification of outburst-prone coal seams, as well as prevention and control measures.

The early identification of potential landslide hazards is of great practical significance for disaster early warning and prevention. The study used different machine learning methods to identify potential active landslides along a 15 km buffer zone on both sides of Jinsha River (Panzhihua-Huize section), China. The morphology and texture features of landslides were characterized with InSAR deformation monitoring data and high-resolution optical remote sensing data, combined with 17 landslide influencing factors. In the study area, 83 deformation accumulation areas of potential landslide hazards and 54 deformation accumulation areas of non-potential landslide hazards were identified through spatial overlay analysis with 64 potential active landslides, which have been confirmed by field verification. The Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were trained and tested through attribute selection and parameter optimization. Among the 17 landslide influencing factors, Drainage Density, NDVI, Slope and Weathering Degree play an indispensable role in the machine learning and recognition of landslide hazards in our study area, while other influencing factors play a certain role in different algorithms. A multi-index (Precision, Recall, F1) comparison shows that the SVM (0.867, 0.829, 0.816) has better recognition precision skill for small-scale unbalanced landslide deformation datasets, followed by RF (0.765, 0.756, 0.741), DT (0.755, 0.756, 0.748) and NB (0.659, 0.659, 0.659). Different from the previous study on landslide susceptibility and hazard mapping based on machine learning, this study focuses on how to find out the potential active landslide points more accurately, rather than evaluating the landslide susceptibility of specific areas to tell us which areas are more sensitive to landslides. This study verified the feasibility of early identification of landslide hazards by using different machine learning methods combined with deformation information and multi-source landslide influencing factors rather than by relying on human–computer interaction. This study shows that the efficiency of potential hazard identification can be increased while reducing the subjective bias caused by relying only on human experts.

Many detailed data on past geological hazard events are buried in geological hazard reports and have not been fully utilized. The growing developments in geographic information retrieval and temporal information retrieval offer opportunities to analyse this wealth of data to mine the spatiotemporal evolution of geological disaster occurrence and enhance risk decision making. This study presents a combined NLP and ontology matching information extraction framework for automatically recognizing semantic and spatiotemporal information from geological hazard reports. This framework mainly extracts unstructured information from geological disaster reports through named entity recognition, ontology matching and gazetteer matching to identify and annotate elements, thus enabling users to quickly obtain key information and understand the general content of disaster reports. In addition, we present the final results obtained from the experiments through a reasonable visualization and analyse the visual results. The extraction and retrieval of semantic information related to the dynamics of geohazard events are performed from both natural and human perspectives to provide information on the progress of events.