Location Of Hazards Research Articles

Projecting Future Chronic Coastal Hazard Impacts, Hotspots, and Uncertainty at Regional Scale

AbstractWhile there is high certainty that chronic coastal hazards like flooding and erosion are increasing due to climate change induced sea‐level rise, there is high uncertainty surrounding the timing, intensity, and location of future hazard impacts. Assessments that quantify these aspects of future hazards are critical for adaptation planning under a changing climate and can reveal new insights into the drivers of coastal hazards. In particular, probabilistic simulations of future hazard impacts can improve these assessments by explicitly quantifying uncertainty and by better simulating dependence structures between the complex multivariate drivers of hazards. In this study, a regional‐scale probabilistic assessment of climate change induced coastal hazards is conducted for the Cascadia region (Northern Washington to Northern California), USA during the 21st century. Three co‐produced hazard proxies for beach safety, erosion, and flooding are quantified to identify areas of high hazard impacts and determine hazard uncertainty under three sea‐level rise scenarios. A novel chronic coastal hazard hotspot indicator is introduced that identifies areas that may experience significant increases in hazard impacts compared to present day conditions. We find that beaches near the California‐Oregon border and in Northern Washington have larger hazard impacts and hazard uncertainty due to their morphologic setting. Erosional hazards, relative to beach safety and coastal flooding, will increase the most in Cascadia during the 21st century under all sea‐level rise scenarios. Finally, we find that hazard uncertainty associated with wave and water level variability exceeds the uncertainty associated with sea‐level rise for most of the 21st century.

Development of Targeted Safety Hazard Management Plans Utilizing Multidimensional Association Rule Mining

Investigating hidden hazards and implementing closed-loop management are essential strategies for accident prevention in the mining industry. This study tackles a key challenge in applying association rule mining to the development of hazard management plans for underground mines. The current approach mainly focuses on hazard description data, often underutilizing critical information such as hazard time and location. To address this, we integrate topic mining with association rule mining to uncover intrinsic association patterns among various attributes of mine safety hazards. Through a systematic analysis of standardized mining hazard attributes, five key analytical dimensions were identified: Hazard Type, Level, Time, Location, and Responsible Units. A topic mining model, utilizing the Biterm Topic Model, was constructed to reduce dimensionality and aggregate hazard description data. Evaluation indicators such as Standard Lift and Difference Degree were proposed, resulting in a multidimensional association rule mining model for mining safety hazards. In this research, 1,387 valid rules were extracted based on hazard inspection data from an underground gold mine in China. The analysis revealed relatively strong associations between hazard location and hazard type, responsible unit, as well as hazard level, with association degrees of 1.934, 1.412, and 1.240, respectively. Additionally, 15 rules with a high degree of differentiation were identified to explore interesting correlations among different attributes. Based on this, corresponding control measures and improvement plans were developed for 19 locations. The results demonstrate that a multidimensional partition-based association rule mining approach for mining safety hazards can significantly enhance the specificity of safety training and improve the efficiency of safety hazard investigation.

Household evacuation decision making during simultaneous events: Hurricane Ida and the COVID-19 pandemic

Protective action decisions are complex and require households to consider a variety of factors including hazard risks, household characteristics, location, and experience. Previous research has examined these decisions in the context of a single hazard; however, it is also necessary to consider scenarios whereby two coupled hazards pose simultaneous risks. This paper examines the decision-making of households when making evacuation and shelter-in-place decisions during two simultaneous events: the 2021 Hurricane Ida and the COVID-19 pandemic. Survey data gathered from six parishes eight months after the storm were used to analyze risk perception tradeoffs and protective action decisions of households before Hurricane Ida made landfall. The results indicate that higher perceived risks of the hurricane causing injury or death, being vaccinated, having a higher income, having children residing in the home, and having previous experience of evacuating increased the odds of evacuating. Likewise, variables such as higher perceived risks related to being hospitalized or killed by COVID-19, being elderly, and being located further away from the storm track all decreased the likelihood of a household undertaking evacuation. The findings of this study improve understanding of how households consider competing risks during simultaneous hazard events, which in turn can help inform strategies for managing future disaster events involving multiple hazards and risks.

In-site experimental study on the effects of infrared thermal imaging technology on levee leakage detection

Excessive seepage significantly affects the stability of levees during flood seasons. Recently, infrared thermal imaging technology has been proposed to detect the seepage leakage of levees for its simplicity, high efficiency and non-contact. However, previous studies mainly focused on theoretical and laboratory tests to verify its effect, which involve many assumptions and simplification. This paper aims to study the practical application effects of infrared thermal imaging technology on levee leakage detection. A typical embankment in Xiangyin County, Hunan Province is selected as the studied area, where a specific point containing water is considered as the potential leakage point. Then, DJI M300RTK UAV equipped with Zenith H20T gimbal is adopted to collect the visible and infrared images of the embankment’s leaking point. The visible and infrared image data collected at different times and at different measurement heights are analyzed to investigate the detection accuracy of infrared thermal imaging technology. Results show that the infrared thermal imaging technology can accurately identify the potential seepage leakage point of levees. Compared with the visible light recognition method, the infrared thermal imaging technology can identify the location and scope of leakage hazards more clearly and accurately at night. During the daytime, infrared detection is less effective than visible light method. Moreover, the measurement altitude between the drone and the target leakage points significantly affects the detection accuracy of infrared thermal imaging technology.

Performance assessment of indicators of a multi-hazards early warning system in an urban mountain region

The urban basins in the mountainous Andean regions are exposed to rapid and poorly planned urbanization processes, causing drastic changes in the landscape, climate, and hydrology. These factors have led to an intensification in the frequency of floods, landslides, falling trees, and droughts, among many other disasters. Therefore, the city of Manizales has implemented an Early Warning System (EWS) to address emergencies and prevent deaths and damage from some of these disasters. However, these hazards can occur simultaneously, further complicating emergency response efforts and requiring the EWS to address multiple hazards; therefore, it has become the Multi-Hazards Early Warning System (MHEWS), which requires indicators and thresholds to generate alerts within sufficient time. The MHEWS implemented in Manizales addresses initially the hazards of floods and landslides, using indicators and thresholds related to recent rainfall and river levels. This article evaluates these proposed indicators by assessing their forecasting capability, effectiveness, and efficiency in increasing warning times, the alarm duration compared to the event duration, and their forecasting ability based on the location of rainfall triggering floods, landslides and other hazards identify. The analyzed indicators focus on hazards and do not consider exposure or vulnerability in communities. The results indicate that a single hazard indicator is not enough, and including multiple indicators based on complementary sources of information improves the performance of MHEWS in addressing emergencies caused by rain.

IDENTIFIKASI BAHAYA DAN PENILAIAN RISIKO DI KAWASAN WISATA LEUWI KENIT, CILETUH PALABUHANRATU UGG

Ciletuh Palabuhanratu as the first geopark in West Java which has been recognized by UNESCO since 2018, is famous as the first land in western Java. One of the natural tourist object in the Geopark area with the beauty of ancient rock structures aged more than 60 million years ago is the Leuwi Kenit natural tourist destination in Pasirpanjang Village, which has a very large number of tourists, but in certain periods there are often visitor accidents. This research aims to identify hazards and risk assessment, as well as risk control from hazards, with research objects including parking areas, entrance gates, access routes, play facilities, and exit routes. The research method is a qualitative Hazard Identification and Risk Assessment and mapping of danger points, literature studies, field observations, interviews with the community, tour guides and tourist attraction guards. Based on the results of the study, there are fifteen danger points in the Leuwi Kenit tourist attraction, some of which can occur when the activity is running with a range of risk relative status, namely low to high. Risk relative high status is recorded to have dominated with seven danger points, risk relative moderate status with six danger points, and risk relative low with two danger points. According to the results of the study, there are fifteen danger points in the Leuwi Kenit tourist attraction, some of which can occur when the activity is running with a range of risk relative status, namely low to high. Risk relative high status is recorded as dominant with seven danger points, risk relative moderate status with six danger points, and risk relative low status with two danger points. The results of the risk assessment show that the risk status is included in potential hazards with high and medium hazard risk levels, management attention and control planning are needed. Alternative risk controls that can be implemented to minimize the occurrence of risk are administrative, engineering, and elimination approaches. Forms of risk avoidance applications such as making paths according to safe standards, making information boards for hazard location points, and rearranging amusement ride according to safety standards at Leuwi Kenit.

Evacuation modelling for rapid multi-hazard tabletop exercise deployment

To prepare for large-scale emergencies and crisis affecting communities, authorities, and emergency commanders use several types of training methods ranging from seminars to full-scale exercises. Within this continuum of exercise types, tabletop exercises (TTXs) are habitually used to familiarise participants with mitigation strategies, population management and evacuation procedures conducted as a response to natural or technological hazards. Commonly, TTXs are paper-based, and if computerised, use basic electronic maps, tend to be scripted and have a linear nature. Information flow is unidirectional as the script dictates how the exercise unfolds. These exercises have little capacity for producing qualitative or quantitative feedback related to the impact that the received scenario injects (i.e., incoming messages including scripted events and hazard locations), the authorities’ decisions, and the impact of hazards have on the wellbeing of the community and the evacuation process. While informative during training, this type of feedback may prove vital in assessing the likely impact of real incident. In this work an evacuation simulation model is proposed to augment the TTX experience in real time, offering feedback and insights on the impact that such injects, decisions and hazards have on the simulated community.The proposed methodology is utilised in an actual TTX co-organised and executed by the Municipality of Rhodes, Greece, where the evacuation model is used to (a) develop the standard, non-incident specific evacuation procedures for the Medieval City of Rhodes (MCR), (b) to adapt these procedures based on the injects (generated on-site or telecommunicated, emulating receipt from the field), producing the TTX scenario and (c) to provide information on the impact that the TTX hazards have on the evacuation process. The integration of evacuation modelling into the TTX process demonstrated that it is possible to gain a deeper understanding of the complexities related to route choices in response to path closures, the assembly and evacuation performance, as well as the management of the simulated incident by analysing qualitative and quantitative simulation results.

GCNet: Ground Collapse Prediction Based on the Ground-Penetrating Radar and Deep Learning Technique

Manual methods to detect and identify potential ground collapse hazards have a high volume of workload and rely on manual skills and experience strongly, with substantial inconsistencies. This paper proposes an automatic recognition method of potential underground hazards and designs an object detection-based algorithm to predict the hidden danger of ground collapse, called GCNet. The GCNet is a ground collapse hazard detection network, which is developed to detect underground hazards, including cavities, poor soil quality, pipelines, and soil background. The GCNet uses the deep residual network ResNet101 and feature pyramid network (FPN) to extract features and a task coordination network (TCN) to identify the category and location of hidden underground hazards. Further, a feature enhancement method based on the regional binary pattern is proposed to improve the accuracy of the proposed model by expanding the ground-penetrating radar (GPR) data by adding multi-level features to GPR images. The experiments are conducted on a large amount of real GPR data and experiment results show that the proposed automatic recognition method can surpass the existing deep learning-based methods in hidden underground hazard recognition and identification.

Communicating Hazard Location through Text-and-Map in Earthquake Early Warnings: A Mixed Methods Study

Communicating Hazard Location through Text-and-Map in Earthquake Early Warnings: A Mixed Methods Study

Assessment of the transferability of European road safety inspection procedures and risk index model to Egypt

Road safety is considered a worldwide issue, especially in developing countries where road fatalities are considered the top cause of death among youth. Generally, three main factors impact road safety including driver, vehicle, and road environment. Statistics show that driver behavior is the major contributory factor to crashes (65%); however, other factors may lead to higher severity crashes such as deteriorated infrastructure, unforgiving roadside design, etc. In this regard, extensive research work has been performed to analyze these crash-contributing factors and propose safety measures. For instance, in North America, researchers developed the Highway Safety Manual (HSM) which provides crash prediction models (CPM) and safety performance functions (SPFs) used in implementing effective safety measures. In the European Union (EU), crash data is complementary to road safety inspections as tools for the safety management of the road network in operation.This research investigates the potential of transferring the European experience, namely the Identification of Hazard Location (IASP) procedures, to Egypt. The analysis shows not only a significant similarity in the safety levels of infrastructure between Egypt and Italy but also in speed behavior. The transferability of the EU IASP procedure is validated by comparing the output of the Risk Index (RI) measure as a surrogate measure of safety with the expected crash frequency resulting from HSM’s SPFs. The comparison is assessed using Spearman’s rank correlation coefficient. This process is applied to a case study that examines a 6-km segment of a two-lane, two-way rural road connecting Faraskour and El Mansoura in Egypt, serving as an example of a hazardous rural road in Egypt. The results indicate that the relation between the RI outputs and the expected crash frequency at the majority of segments of the road section is significant based on Spearman’s rank correlation factor value of 0.75. Few limitations have been identified and presented in the study including the effect of access located on curves or hidden in vegetated areas.

Identifying firefighters' situation awareness requirements for fire and non-fire emergencies using a goal-directed task analysis

A firefighter's situation awareness (SA) is considered crucial to making effective tactical decisions and actions at the scene. Despite the importance of the firefighter's SA, there have been limited research efforts to understand what cues and information firefighters use to assess ongoing situations and predict future conditions. In addition to fire events, contemporary firefighters respond to an increasing volume of non-fire incidents. Thus, this study aims to identify firefighters' SA during three fire incidents (single house fire, vehicle fire, and passenger aircraft fire) and three non-fire incidents (medical emergency, hazardous materials, and urban search and rescue). A goal-directed task analysis was conducted via focus group discussions with eight career firefighters. Findings indicate that firefighters build their SA by processing various cues from hazards (e.g., fire, ignition source), humans (e.g., occupants, bystanders, drivers, passengers), spatial elements (e.g., building structure, location of hazards), and surrounding conditions (e.g., traffic, weather). Our findings provide insights into SA measurement, SA-oriented work processes, training for SA, and designing technologies to support firefighters' SA during all-hazard responses.

An approach for facilities manager to reducing risks in gas pipelines through sensors and data intelligentization in BIM

Purpose Gas transmission pipelines are at constant risk of gas leakage or fire due to various atmospheric environments, corrosion on pipe metal surfaces and other external factors. This study aims to reduce the human and financial risks associated with gas transmission by regularly monitoring pipeline performance, controlling situations and preventing disasters. Design/methodology/approach Facility managers can monitor the status of gas transmission lines in real-time by integrating sensor information into a building information modeling (BIM) 3D model. Using the Monitoring Panel plugin, coded in C# programming language and operated through Navisworks software, the model provides up-to-date information on pipeline safety and performance. Findings By collecting project information on the BIM and installing critical sensors, this approach allows facility manager to observe the real-time safety status of gas pipelines. If any risks of gas leakage or accidents are identified by the sensors, the BIM model quickly shows the location of the incident, enabling facility managers to make the best decisions to reduce financial and life risks. This intelligent gas transmission pipeline approach changes traditional risk management and inspection methods, minimizing the risk of explosion and gas leakage in the environment. Originality/value This research distinguishes itself from related work by integrating sensor data into a BIM model for real-time monitoring and providing facility managers with up-to-date safety information. By leveraging intelligent gas transmission pipelines, the system enables quick identification and location of potential hazards, reducing financial and human risks associated with gas transmission.

Automatic Identification of Earth Rock Embankment Piping Hazards in Small and Medium Rivers Based on UAV Thermal Infrared and Visible Images

Piping is a major factor contributing to river embankment breaches, particularly during flood season in small and medium rivers. To reduce the costs of earth rock embankment inspections, avoid the need for human inspectors and enable the quick and widespread detection of piping hazards, a UAV image-acquisition function was introduced in this study. Through the collection and analysis of thermal infrared and visible (TIR & V) images from several piping field simulation experiments, temperature increases, and diffusion centered on the piping point were discovered, so an automatic algorithm for piping identification was developed to capture this phenomenon. To verify the identification capabilities, the automatic identification algorithm was applied to detect potential piping hazards during the 2022 flooding of the Dingjialiu River, Liaoning, China. The algorithm successfully identified all five piping hazard locations, demonstrating its potential for detecting embankment piping.

Rainfall extremes under future climate change with implications for urban flood risk in Kathmandu, Nepal

Increased rainfall extremes cause severe urban flooding in cities with adverse socio-economic consequences, and Kathmandu city is no exception. Rainfall events are projected to become more intense and frequent in a warm and wet future, and they pose a major challenge to the sustainable development of Kathmandu city. This paper analyses historical extreme rainfall patterns across the city and uses these as the basis for future projections in combination with a range of General Circulation Models. Future projections of extreme rainfall are then fed into the numerical flood model HAIL-CAESAR (Lisflood), using a high-resolution digital elevation model of Kathmandu. We show that rainfall intensity, such as the annual maximum 1-day rainfall (RX1day), is projected to increase by up to 72% in the future, and the historical 100-year return period rainfall will become a 20 or 25-year return period rainfall. The flood modelling results show that the future flood hazard (magnitude and extent) will increase. The historical 100-year return period flood discharge will correspond to a 25-year return period future flood. A 100-year period flood discharge is likely to increase up to 72% (37% median) in the future. Area of land inundated by more than 1 m in a 100-year return period flood event could increase from 11.7 km2 to 23 km2 in the future. Furthermore, the location and timing of rainfall maxima affect the peak, timing, and location of flood hazards. This analysis can serve as a scientific basis to assess future flood-induced risk in Kathmandu in response to climate change.

Landslide Susceptibility Mapping in Ampang Jaya: Comparing Probabilistic Method and Weighted Overlay Method

When it came time to produce the map, the entire district of Ampang Jaya was chosen as the area for the research. These two approaches gave very different results in terms of accuracy. The results of the map were categorised using a range of different classification techniques, some of which include Natural Breaks and Equal Interval, respectively. Various landslide susceptibility hazard zoning results were found when the two different classification systems were applied to the data. These zoning areas need to go through an accuracy assessment process in order to be validated. The accuracy of the model was evaluated by comparing the findings about the historical locations of landslide occurrences with the findings regarding the susceptibility hazard locations of potential landslide areas. Because the accuracy of the Probabilistic technique was 66.67% for Equal Interval and 73.33% for Natural Breaks, respectively, this demonstrates that the Probabilistic method produces more dependable results than the Weighted Overlay method produces. Therefore, the accuracy assessment results indicate that the probabilistic method provides more reliable information for accuracy assessment than the Weighted Overlay method. This is due to the fact that the result of the accuracy assessment obtained from Weighted Overlay is significantly lower than that obtained from Probabilistic for each classification technique.

Predicting Models for Local Sedimentary Basin Effect Using a Convolutional Neural Network

Although the numerical models can estimate the significant influence of local site conditions on the seismic propagation characteristics near the surface in many studies, they cannot feasibly predict the seismic ground motion amplification in regular engineering practice or earthquake hazard assessment due to the high computational cost and their complex implementation. In this paper, the scattering problem of trapezoidal sedimentary basins, one of the representatives of local complex sites with a relatively small model size, and simplified by practice in this type of study, was selected as the basin model. A series of standard basin models were built to quantify the relationship between the site condition parameters and the site amplification parameters (the peak ground acceleration and the hazard location). In addition, the factors that influence seismic ground motion amplification, such as the basin shape ratio, the soil depth, the basin edge dip angle, the ratio of shear wave velocity between the bedrock and the soil layer, the damping coefficient, and the fundamental frequency, were selected to investigate the sensitivity. A convolutional neural network (CNN) algorithm based on deep learning replacing traditional recursive algorithms was explored to establish a prediction model of basin amplification characteristics. By the Bayesian optimization method, the structural parameters of the CNN predicting model were selected to improve the accuracy of the prediction model. The results show that the optimized CNN models could predict the amplification characteristics of the basin better than the un-optimized CNN models. Three prediction models were established with the site condition parameters as the input parameters and their output parameters were the maximum amplification value of the peak ground acceleration (PGA), the hazard location, and their combination for each basin. To analyze the CNN’s prediction ability, each CNN model used about 80% of the data from the seismic model repose results for training and the remaining data (20%) for testing. By comparing the CNN prediction results with the FE simulation results, the accuracy and rationality of each prediction model were studied. The results show that, compared to a single numerical model, the CNN prediction results of the site amplification features could be quickly obtained by inputting the relevant parameters. Compared to recursive class models, the established CNN prediction model can directly establish the relationships among multiple input and multiple output parameters. A comparison of the three kinds of CNN models shows that the prediction accuracy of the joint parameter model was slightly lower than that of the two single-output models.

Wearable EEG-based construction hazard identification in virtual and real environments: A comparative study

Hazard identification on construction jobsites is one of the most critical components of construction safety management. To overcome the limitations in the current manual practice, researchers explored the correlation between hazard perception and changes in workers’ electroencephalogram (EEG) signals collected from wearable sensors to identify and differentiate different types of hazards. However, most of such studies are based on experiments in a virtual reality (VR) environment. The unproven applicability of the VR-led results to the real jobsites and the potential discrepancies in results between the physical and virtual environments are the main barriers toward EEG-based ubiquitous hazard identification on the jobsites. To bridge the research gaps, this paper tests the feasibility of identifying construction hazards in physical and VR environments, and compares their results to identify discrepancies. Two experimental environments (physical and VR, respectively), each containing three hazards, were designed with the same configuration. Hazard identification task was performed in both experiments, data (e.g., EEG signals) was collected and preprocessed, and corresponding features were extracted. Hotspot analysis was conducted to infer the existence of hazards by assessing spatial associations. The results showed that both physical and VR settings were effective for hazard detection, while a better performance was observed in the VR setting. In addition, the potential of developing hazard zones as well as particular hazard locations and a gradual increase in subjects’ perceived hazard level were revealed. The results highlight the opportunities of revealing potential construction hazards on the construction jobsite using wearable EEG.

Planned relocation due to landslide-triggered tsunami risk in recently deglaciated areas

Climate change is contributing to the magnitude, frequency and location of natural hazards, including landslides and landslide-triggered tsunamis. As the costs of protecting against a given risk increase, relocation may become the only feasible option despite the socio-economic, human security and cultural consequences. The relocation of people represents one of the most complex governance challenges generated by climate change. This article contributes to the literature by presenting insights and lessons from two case studies of unprecedented landslide-triggered tsunami risk in recently deglaciated areas that have not previously been described in the relocation literature: the unstable Svínafellsheiði slopes in south-east Iceland, and Karrat and Uummannaq Fjords in north-west Greenland. Our results draw attention to the need for planned relocation to be conducted in-line with international best practices, including those relating to the active involvement of affected people in decision-making, ensuring adequate compensation, and clarifying relocation planning schedules. This has occurred against a backdrop of colonial power dynamics, urbanisation trends, and the rise of tourism in these locations. Based on the findings, we recommend that the role of government pivot from determining risk management and relocation options, to providing a structure to underpin and support community agency.

Determination of underground hazard location using machine learning techniques

ABSTRACT Underground mine accidents, such as mine fires, remain a concern for mine operators, posing a health and safety risk to mine workers. Dealing with an unknown location of an accident underground can be a challenging task, creating hazardous conditions for miners during an evacuation and rescue operation. Timely determination of an underground fire event location and size is of great importance in reducing the risk of injuries. Machine learning (ML) has made its way into mining, enabling the development of data-driven predictive models that can be applied to miner health and safety problems. A new methodology has been developed using the application of an ML technique to characterize underground accidents such as the size and location of an underground mine fire using the post-fire airflow data. This paper describes the methodology and its verification through examples. The National Institute for Occupational Safety and Health (NIOSH) is endeavoring to develop workplace solutions to improve the detection of and reduce the risk of hazardous conditions. The results demonstrate a promising application of ML-based models and provide a useful tool for solving the problem of unknown fire locations and reducing the risk of hazardous conditions.

Atmospheric release of organic solvents due to hazardous events in the paints and varnishes industry

Technogenic risk is the result of creation and development of hazards that originate from accidents in the technosphere. It involves emissions of hazardous industrial substances into the work and natural environments, fires, radioactive contamination, and contamination by toxic substances during their transport and storage. The qualitative and quantitative technogenic risk assessment has to be performed during the use, handling, transport, and storage of hazardous substances if it is decided that specific environmental elements at a specific location are likely to become exposed to hazardous substances, leading to environmental degradation. This paper discusses vulnerability zones - specifically, the high lethality zone and the irreversible effects zone - that are formed due to hazardous events or accidents in the paints and varnishes industry. Accidents are discussed in terms of exposure to solvents classified as hazardous to ecosystem components, the biosphere, anthroposphere, and the human population during their atmospheric dispersion. The vulnerability zones are designated using the REHRA (Rapid Environmental and Health Risk Assessment) methodology and their spatial arrangement is used to perform the risk assessment for the purpose of notifying authorized institutions and the public about a potentially increased risk at the hazard location and/or the immediate vicinity.