Hazard Prevention Research Articles

GIS-based MCDM approach for landslide hazard zonation mapping in east Gojjam zone, central Ethiopia

Landslides are prevalent in the Ethiopian highlands, particularly in the east Gojjam zone, which is highly affected by landslide problems. This research was carried out in the east Gojjam zone, northwestern Ethiopia. The study area is part of an economically important area in the country, and it is the main source of water for the Grand Ethiopian Renaissance Dam (GERD). The main objective of this work was to undertake a detailed inventory of past landslide locations and prediction of present and future landslide hazards, as well as the preparation of a landslide zonation map in the East Gojjam zone by using the Analytical Hierarchy Process (AHP) with the GIS technique. The parameters used for this study were slope degree, slope aspect, land use and land cover, road proximity, rainfall, lithology, altitude, and river proximity. The various causative parameters were collected from the field, and suitable modifications were made to the thematic maps. Finally, the ratings for various parameters were used as the basis to prepare the LHZ map in GIS windows. The landslide susceptibility and inventory mapping were produced in the GIS environment. The results of the study show that the main driving factors for the landslide hazards in the area were river proximity, rainfall, and manmade activities. Validation of this LHZ map revealed that more than 80% of past landslides match within the "high hazard zone" and reasonably accepted the rationality of the adopted methodology. The considered parameters, as well as their evaluation of the production of LHZ-Map, were confirmed. The produced landslide inventory map is very important for urban planners, agricultural studies, environmentalists, and future landslide hazardous prevention and mitigation strategies.

The role of social capital in building community disaster resilience –empirical evidences from rural China

The paper investigates the role of social capital in hazard management in a case area which was overwhelmingly swept by flood in mid-2021. The interplay among social capital, hazard preparedness, response and recovery are explored by using structure equation modeling. Data were gathered from over 413 villages from August 2021 to September 2021, which is one month after the flood occurred. Research findings show that villagers in region with abundant social capital are more likely to contribute to hazard preparedness. We have also confirmed the positive role of social capital in restraining the pursuit of self-interests. Villagers are more willing to help others, and take care of vulnerable population when the hazard strikes. However, it is surprising to note that social capital fails to motivates residents to make preparedness plans for future risks. This retreat effect exists when big loss occurred and failed to meet local people's expectations. Overall, this study implies that the impact of social capital on hazard risk management is extremely complex and such complexity should not be attribute solely to divergent forms of network relationship. The above conclusion enables policy makers to pay attention to the psychological foundation of social capital while cultivating it.

Risk of Natural Hazards Caused by Extreme Precipitation in Poland in 1951–2020

Extreme precipitation of a minimum daily value of >30 mm often initiates natural hazards such as floods, which in turn may not only lead to property damage but also present a danger to people’s health and lives. This paper mainly focuses on examining the trends and frequency of extreme daily precipitation (EDPr) in Poland. Also, it determines natural risk zones caused by EDPr of >30 mm, >50 mm, >70 mm, and >100 mm. In Poland, a significant positive trend was found for EDPr > 30 mm, >50 mm, and >70 mm in September, and for EDPr >100 mm in May. The most frequently recorded EDPr in Poland was >30 mm, the frequency of which ranged from 0.04% in February to nearly 3% in July. EDPr of >100 mm was recorded in 4 months, from May to August. An increase in the frequency of monthly EDPr in Poland occurred mainly in the southwestern and western parts. In Poland, three hazard zones of various frequencies of EDPr events were determined. In Zone III, which is in the southwestern and southern parts of the country, EDPr events occurred far more often than in Zone I; on average, four times more in the spring–summer season and slightly more than five times more in the autumn–winter season. The obtained results may help in the building of modern management and monitoring systems for the prevention of natural hazards caused by extreme precipitation.

A review of submarine landslides associated with rapid sedimentation

Submarine landslides are a critical geological process that significantly influences seafloor stability and geological evolution, making them a focal point of geological disaster research. Particularly, rapid sedimentation as a crucial preconditioning factor for submarine landslide initiation is increasingly recognized in contemporary research. However, no comprehensive review currently examines the correlation between rapid sedimentation and submarine landslides. To elucidate the interaction mechanisms between submarine landslides and rapid sedimentation and to provide a new theoretical foundation for the prediction and prevention of geological hazards, the study categorizes three primary sedimentation patterns in marine environments and establishes a detailed historical database of sedimentation-related submarine landslide events. Moreover, the paper systematically reviews and critiques the existing research methods and advancements in addressing rapid sedimentation-related submarine landslides, and a bibliometric analysis of recent research progress is conducted. Finally, the study highlights several challenges in the study of rapid sedimentation and proposes future recommendations, aiming to provide robust support for the in-depth investigation of submarine landslides.

Risk Assessment of Landfalling Tropical Cyclones in China Based on Hazard Risk Theory

As a frequent hazard, tropical cyclones have a great impact on the social and economic development of China, which is close to the origin of tropical cyclones in the western Pacific Ocean. The primary objective of this study was to construct a comprehensive risk assessment model for tropical cyclone hazards based on natural influencing factors, informing recommendations for hazard prevention and mitigation in affected regions. This research focused on tropical cyclones that made landfall in mainland China and Hainan from 1949 to 2023, utilizing hazard risk theory and classical extreme value theory. The wind speed and rainfall data during the peak cyclone periods (June to October) from 1997 to 2021 gathered from various meteorological stations, as well as altitude and vegetation cover data, were examined. Hierarchical analysis and ArcGIS spatial analysis methods were employed to study the characteristics of the spatiotemporal distribution of landfalling tropical cyclones and the comprehensive risk of tropical cyclone hazards, and the regions of China were delineated according to these methods. The results showed that, during the period from 1949 to 2023, the overall number of landfalling tropical cyclones decreased in a fluctuating manner, while the intensity of the cyclones increased. Furthermore, severe typhoons tended to occur more frequently in the summer than autumn with time, intensifying the challenge to resist short-term hazards. Moreover, the hazard-causing factors in areas affected by tropical cyclones displayed an increasing trend from north to south and from west to east. In detail, the regions sensitive to natural hazards were primarily located in the central part of Liaoning province, Tianjin, central and eastern Hebei province, Shandong province, eastern Henan province, central and northern Anhui province, Jiangsu province, and Shanghai, which are characterized by flat terrain and relatively low vegetation cover. Overall, the comprehensive risk of tropical cyclone hazards showed a geographical distribution that decreases from south to north and from east to west, with coastal cities in provinces such as Hainan, Guangdong, Guangxi, Fujian, and Zhejiang—including Haikou, Zhanjiang, Xiamen, Beihai, and Taizhou—exhibiting the highest levels of risk.

Geological hazard susceptibility assessment based on different mathematical statistical models: a case study of Zhejiang province

Geological disaster is a great threat to people's life and property safety, how to predict it reliably and divide it according to the susceptibility of geological disaster is of great significance. Based on the historical collapse data of Zhejiang Province, this paper selects the evaluation indexes of water flow intensity index (SPI), terrain wetness index (TWI), land use type, slope aspect, slope, annual rainfall, river network density, elevation, road density, plane curvature, normalized difference vegetation index (NDVI) and other factors. Based on the frequency ratio model (FR), random forest model (RF) and logistic regression model (LR), the susceptibility zoning of geological disasters in Zhejiang Province is carried out. The results show that: (1) The high and extremely high risk areas in Zhejiang province are mainly distributed near the river system and road network in the high altitude area; (2) In terms of the evaluation effect of comprehensive PR curve, the random forest model has a better evaluation effect than other models; (3) With the increase of the vulnerability level, the proportion of disasters under each model zoning is gradually increasing, indicating that the zoning effect of each model is good; 4) Quzhou city, Jinhua City, Wenzhou city and Shaoxing city in Zhejiang Province account for a large proportion of the high susceptibility areas and above. The results of vulnerability zoning can provide reference for geological hazard prevention and land planning in Zhejiang province.

An Automated Approach for Mapping Mining-Induced Fissures Using CNNs and UAS Photogrammetry

Understanding the distribution and development patterns of mining-induced fissures is crucial for environmental protection and geological hazard prevention. To address labor-intensive manual inspection, an automated approach leveraging Convolutional Neural Networks (CNNs) and Unmanned Aerial System Photogrammetry (UASP) is proposed for fissure identification and mapping. Initially, the ResNet-50 network was employed for the binary classification of the cropped UASP orthophoto images. A comparative analysis was conducted to determine the optimal model between DeepLabv3+ and U-Net. Subsequently, the identified fissures were mosaicked and spatially projected onto the original orthophoto image, incorporating precise projection data, thereby furnishing a spatial reference for environmental governance. The results indicate a classification accuracy of 93% for the ResNet-50 model, with the U-Net model demonstrating a superior identification performance. Fissure orientation and distribution patterns are influenced by the mining direction, ground position of the mining workface, and topographic undulations. Enhancing the CNN performance can be achieved by incorporating variables such as slope indices, vegetation density, and mining workface locations. Lastly, a remote unmanned approach is proposed for the automated mapping of mining-induced fissures, integrated with UAS automated charging station technology. This study contributes to the advancement of intelligent, labor-saving, and unmanned management approaches advocated by the mining industry, with potential for broad applications in mining environmental protection efforts.

Analysis of cracks development and damage evolution in red sandstone under dry-wet cycles based on temporal and frequency characteristics of acoustic emission

Utilizing the goafs of underground reservoirs (underground buildings) in western coal mines as storage spaces plays a crucial role in ecological environment protection and energy transition in mining areas. Hence, it is crucial to recognize the repeated erosion of reservoir water levels on the deformation and damage mechanisms of the roof and floor rock masses in the underground building dams. This paper intends to study the instability failure mechanisms and precursor responses of red sandstone samples with 0–3 dry-wet cycles during uniaxial loading-unloading under acoustic emission (AE) monitoring. The AE time-frequency features were analyzed, and the crack three-dimensional spatial extension and tensile-shear crack evolution were explored. The b-value and entropy value of AE events were presented for evaluating crack expansion damage status during loading-unloading. The findings suggest that the peak strength, elastic modulus, and crack thresholds of red sandstone exhibit a positive exponential decline with growing dry-wet cycles. Conversely, the peak strain and strain-softening modulus exhibit an opposite trend. With growing dry-wet cycles, AE signals from low-frequency, high-amplitude, and large-scale cracks decline, exhibiting a multi-frequency simultaneous response effect before sample instability. Meanwhile, the proportion and concentration of localization events with energy exceeding 104 aJ rise, and the failure instability gradually shifts from tensile-shear failure to tensile cracks. The sudden decline in b-value and the significant fluctuation in entropy value serve as short-term precursors to imminent rock instability failure, with the system energy following an ordered-disordered-ordered pattern. The research findings offer certain experimental foundation and novel judgment indicators for rock structure safety warning, including the stability of underground building dams, support for water-rich roadways, water hazard prevention, and utilization of abandoned mine spaces.

WORK INJURY RATE IN THE SLOVAK RAILWAYS COMPANY IN CONNECTION WITH WORK SAFETY

Like all branches of our economy, also railway transport is based on long-term strategic plans whose aim is to create a transportation policy in order to achieve the most appropriate and equal conditions for all carriers. Coping with safety and hazard prevention must be based mainly on the capabilities of each business entity. The paper points out the issue of employees’ work safety in the Slovak Railways company in connection with the European legislation in the field of safety and health protection at work and its implementation into the laws of the Slovak Republic. It also discusses the issues of employees’ safety at workplaces and summarizes the duties of employees and employers in the field of health and safety protection in railway transport and deals with the implementation of European legislation into the laws of the Slovak Republic. The paper also analyses the safety of employees in railway transport in the form of a survey, whose aim is to determine the state of employees’ safety at workplaces and aims to confirm or refute hypotheses regarding the impact of the quality and effectiveness of internal safety regulations of the Slovak Railways on the safety of employees and also whether the employer implements sufficient measures in order to minimize possible accidents at workplaces.

Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques

Frequent mountain torrent disasters have caused significant losses to human life and wealth security and restricted the economic and social development of mountain areas. Therefore, accurate identification of mountain torrent hazards is crucial for disaster prevention and reduction. In this study, based on historical mountain torrent hazards, a mountain torrent hazard prediction model was established by using Bayesian Model Average (BMA) and three classic machine learning algorithms (gradient-boosted decision tree (GBDT), backpropagation neural network (BP), and random forest (RF)). The mountain torrent hazard condition factors used in modeling were distance to river, elevation, precipitation, slope, gross domestic product (GDP), population, and land use type. Based on the proposed BMA model, flood risk maps were produced using GIS. The results demonstrated that the BMA model significantly improved upon the accuracy and stability of single models in identifying mountain torrent hazards. The F1-values (comprehensively displays the Precision and Recall) of the BMA model under three sets of test samples at different locations were 3.31–24.61% higher than those of single models. The risk assessment results of mountain torrents found that high-risk areas were mainly concentrated in the northern border and southern valleys of Yuanyang County, China. In addition, the feature importance analysis result demonstrated that distance to river and elevation were the most important factors affecting mountain torrent hazards. The construction of projects in mountainous areas should be as far away from rivers and low-lying areas as possible. The results of this study can provide a scientific basis for improving the identification methods of mountain torrent hazards and assisting decision-makers in the implementation of appropriate measures for mountain torrent hazard prevention and reduction.

A Knowledge-Guided Approach for Landslide Susceptibility Mapping Using Convolutional Neural Network and Graph Contrastive Learning

Landslide susceptibility mapping (LSM) constitutes a valuable analytical instrument for estimating the likelihood of landslide occurrence, thereby furnishing a scientific foundation for the prevention of natural hazards, land-use planning, and economic development in landslide-prone areas. Existing LSM methods are predominantly data-driven, allowing for significantly enhanced monitoring accuracy. However, these methods often overlook the consideration of landslide mechanisms and uncertainties associated with non-landslide samples, resulting in lower model reliability. To effectively address this issue, a knowledge-guided landslide susceptibility assessment framework is proposed in this study to enhance the interpretability and monitoring accuracy of LSM. First, a landslide knowledge graph is constructed to model the relationships between landslide entities and summarize landslide susceptibility rules. Next, combining the obtained landslide rules with geographic similarity principles, high-confidence non-landslide samples are selected to optimize the quality of the samples. Subsequently, a Landslide Knowledge Fusion Cell (LKF-Cell) is utilized to couple landslide data with landslide knowledge, resulting in the acquisition of informative and semantically rich landslide event features. Finally, a precise and credible landslide susceptibility assessment model is built based on a convolutional neural network (CNN), and landslide susceptibility spatial distribution levels are mapped. The research findings indicate that the CNN-based model outperforms traditional machine learning algorithms in predicting landslide probability; in particular, the Area Under the Curve (AUC) of the model was improved by 3–6% after sample optimization, and the AUC value of the LKF-Cell method was 6–11% higher than the baseline method.

Instrumental Monitoring of a Slow-Moving Landslide in Piedmont (Northwest Italy) for the Definition of Rainfall Thresholds.

The prediction and prevention of landslide hazard is a challenging topic involving the assessment and quantitative evaluation of several elements: geological and geomorphological setting, rainfalls, and ground motion. This paper presents the multi-approach investigation of the Nevissano landslide (Asti Province, Piedmont, NW Italy). It shows a continuous and slow movement, alongside few paroxysmal events, the last recorded in 2016. The geological and geomorphological models were defined through a field survey. An inventory of the landslide's movements and rainfall records in the period 2000-2016 was performed, respectively, through archive investigations and the application of "Moving Sum of Daily Rainfall" method, allowing for the definition of rain thresholds for the landslide activation (105 mm and 193 mm, respectively, in 3 and 30 days prior to the event). The displacements over the last 8 years (2016-2023) were monitored through an innovative in-continuum monitoring inclinometric system and Earth Observation (EO) data (i.e., relying on Interferometric Synthetic Aperture Radar, or InSAR data): it gave the opportunity to validate the rainfall thresholds previously defined. This study aims to provide information to public authorities for the appropriate management of the site. Moreover, the proposed workflow could be adopted as a guideline for investigating similar situations.

Neural network analysis of mechanization's impact on coal miner's occupational health

In order to clarify the transmission mechanism of the impact of mechanization on the occupational health of miners and to provide empirical evidence for the development of new quality productivity in the coal industry that balances health and efficiency. In August 2022, we selected a typical coal mine, constructed a comprehensive evaluation index of miners' occupational health through a questionnaire survey based on the fully connected neural network model. A Bayesian model was used to verify the influence of mechanization level on miners' occupational health. We found that: the predicted probability of occupational diseases could be used as a comprehensive indicator of the level of occupational health, providing a basis for early intervention and prevention of occupational diseases. Mechanization could directly promote the improvement of miners' occupational health level, and also indirectly affect occupational health level by influencing hazards level and work intensity. The indirect effect of mechanization on work intensity was positive, and the indirect effect of mechanization on hazards level was positive. Presented the "inverted U-shaped" process in the mechanization breakthrough semi-mechanized level would realize the economies of scale of health protection, its impact on the prevention and control of occupational hazards would turn from negative to positive.

Height Prediction of Water-Conducting Fracture Zone in Jurassic Coalfield of Ordos Basin Based on Improved Radial Movement Optimization Algorithm Back-Propagation Neural Network

The development height of the water-conducting fracture zone (WCFZ) is crucial for the safe production of coal mines. The back-propagation neural network (BP-NN) can be utilized to forecast the WCFZ height, aiding coal mines in water hazard prevention and control efforts. However, the stochastic generation of initial weights and thresholds in BP-NN usually leads to local optima, which might reduce the prediction accuracy. This study thus invokes the excellent global optimization capability of the Improved Radial Movement Optimization (IRMO) algorithm to optimize BP-NN. The influences of mining thickness, coal seam depth, working width, and hard rock lithology proportion coefficient on the height of WCFZ are investigated through 75 groups of in situ data of WCFZ heights measured in the Jurassic coalfield of the Ordos Basin. Consequently, an IRMO-BP-NN model for predicting WCFZ height in the Jurassic coalfield of the Ordos Basin was constructed. The proposed IRMO-BP-NN model was validated through monitoring data from the 4−2216 working faces of Jianbei Coal Mine, followed by a comparative analysis with empirical formulas and conventional BP-NN models. The relative error of the IRMO-BP-NN prediction model is 4.93%, outperforming both the BP-NN prediction model, the SVR prediction model, and empirical formulas. The results demonstrate that the IRMO-BP-NN model enhances the accuracy of predicting WCFZ height, providing an application foundation for predicting such heights in the Jurassic coalfield of the Ordos Basin and protecting the ecological environment of Ordos Basin mining areas.

Optimizing Residential Construction Site Selection in Mountainous Regions Using Geospatial Data and eXplainable AI

The rapid urbanization of Abha and its surrounding cities in Saudi Arabia’s mountainous regions poses challenges for sustainable and secure development. This study aimed to identify suitable sites for eco-friendly and safe building complexes amidst complex geophysical, geoecological, and socio-economic factors, integrating natural hazards assessment and risk management. Employing the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP), the study constructed a suitability model incorporating sixteen parameters. Additionally, a Deep Neural Network (DNN) based on eXplainable Artificial Intelligence (XAI) conducted sensitivity analyses to assess the parameters’ influence on optimal location decision making. The results reveal slope as the most crucial parameter (22.90%), followed by altitude and land use/land cover (13.24%), emphasizing topography and environmental considerations. Drainage density (11.36%) and rainfall patterns (9.15%) are also significant for flood defense and water management. Only 12.21% of the study area is deemed “highly suitable”, with “no-build zones” designated for safety and environmental protection. DNN-based XAI demonstrates the positive impact of variables like the NDVI and municipal solid waste generation on site selection, informing waste management and ecological preservation strategies. This integrated methodology provides actionable insights for sustainable and safe residential development in Abha, aiding informed decision making and balancing urban expansion with environmental conservation and hazard risk reduction.

Analysis of the Impact of the Former Abandoned Dregs of Mountain Highways on the Construction and Safety of New Bridge Pile Foundations

In highway reconstruction and expansion projects, the impact of the abandoned dreg body produced by the original highway on the new bridge project cannot be ignored. If the abandoned dregs are not handled properly, they will cause great threat to the operation of the new highway as well as to the safety of the surrounding environment and personnel. In this paper, based on a South China highway reconstruction and expansion project, a model of the abandoned dreg body, bridge pile and anti-slide pile is established with the help of finite difference software FLAC3D 5.0. The calculation results show that compared with burying the anti-slide pile, slope cutting is more effective for improving the stability of the abandoned dreg body. Under the action of vertical force intensity, the displacement of the top of the pile increases continuously, and the maximum bending moment of the pile body increases firstly and then decreases. The pile displacement and bending moment of the bridge pile increase significantly in the storm condition compared with the normal condition. In addition, the abandoned dreg body hazard prevention and control technical measures are studied. The impacts of different locations of anti-slide pile burial on bridge piles are analyzed and compared, and it is determined that the optimal location of anti-slide pile burial is the middle of the secondary slope.

Per- and polyfluoroalkyl substances alter innate immune function: evidence and data gaps

Per- and polyfluoroalkyl substances (PFASs) are a large class of compounds used in a variety of processes and consumer products. Their unique chemical properties make them ubiquitous and persistent environmental contaminants while also making them economically viable and socially convenient. To date, several reviews have been published to synthesize information regarding the immunotoxic effects of PFASs on the adaptive immune system. However, these reviews often do not include data on the impact of these compounds on innate immunity. Here, current literature is reviewed to identify and incorporate data regarding the effects of PFASs on innate immunity in humans, experimental models, and wildlife. Known mechanisms by which PFASs modulate innate immune function are also reviewed, including disruption of cell signaling, metabolism, and tissue-level effects. For PFASs where innate immune data are available, results are equivocal, raising additional questions about common mechanisms or pathways of toxicity, but highlighting that the innate immune system within several species can be perturbed by exposure to PFASs. Recommendations are provided for future research to inform hazard identification, risk assessment, and risk management practices for PFASs to protect the immune systems of exposed organisms as well as environmental health.

Enhancing landslide hazard prevention: Mapping vulnerability via considering the effects of human factors

Vulnerability mapping is necessary to reduce the effects of a landslide disaster. This study presents an approach to map vulnerability induced by a landslide hazard. The vulnerability was assessed by improved multi-criteria decision making (MCDM), in which the effects of human factors were incorporated. In the proposed approach, the weights of the indicators and factors were calculated using an integration method, including a grey decision-making trial and evaluation laboratory and the analytical hierarchy process (AHP) to calculate each index and factor's weight. The effect of human factors is expressed as the ability of residents to cope with hazards in a community, which is evaluated by the adaptability index layer. The adaptability index layer includes four factors: gender ratio, age, education and medical resources. VIKOR is used to score and rank the adaptability index layer. The vulnerability is then mapped using geographic information system (GIS). A case study of historical landslides in Heyuan, China, was used for validation. The results demonstrate that 17.91 % of Heyuan has the highest vulnerability, which is higher than that from the traditional method (13.76 %). The model, considering adaptability, can predict areas with high vulnerability more accurately. Management recommendations are proposed via the following four specific aspects: i) urban vulnerability management and planning; ii) inter-regional differences in socio-economic vulnerability; iii) individual perceptions of landslides and iv) enhancing urban planning and strategies in similar cities.

Enhancing Construction Site Safety: Natural Language Processing for Hazards Identification and Prevention

Enhancing Construction Site Safety: Natural Language Processing for Hazards Identification and Prevention

Neuverteilung von Verantwortung? Brandschutzerziehung als Strategie zur Zukunftssicherung Freiwilliger Feuerwehren

Volunteer firefighters are an established institution in many rural areas across Europe. In spite of this ubiquity, many volunteer brigades have been increasingly facing challenges in discharging their functions. Issues such as undertaking necessary investments, and ensuring daytime availability have become particularly critical against the backdrop of an increase in the number of interventions. Many of the measures intended to futureproof volunteer firefighting are based on transferring responsibility to different actors. To this end developing and expanding the reach of fire safety education functions to enhance the public’s prevention and self-protection skills. The aim of this paper is to explore the process of institutional change induced by innovative initiatives, which aim to advance fire safety education. For the study, we draw on empirical research, which follows the development and mainstreaming of two projects in Austria and Germany. These initiatives aim to standardise disparate approaches and are underpinned by new forms of multi-level cooperation. Our inductive analysis identifies five key steps which define the successful long-term implementation of projects in coproduced services of general interest. We conclude with observations on the redistribution of responsibility for fire services and hazard prevention in contemporary societies.