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In recent years, coal and gas outburst disasters are still occurring and difficult to prevent, seriously endangering the safety of coal mine production. It is well known that the transporting and crushing of outburst coal is the main pathway of energy dissipation during the coal and gas outburst process. However, a consensus regarding how much gas involves in outburst and affects energy dissipation is still lacking. Quantitative study on the gas effect on migration and fragmentation characteristics of outburst coal in restricted roadway space can improve the energy model and guide the prevention and control of gas outburst. In this paper, an improved visual coal and gas outburst dynamic effect simulation experiment system was used to conduct outburst simulation experiments at different gas pressure conditions. The results showed that the movement of outburst coal in the roadway has experienced various flow patterns. In the initial stage of the outburst, under low gas pressure condition, the motion of the outburst coal was dominated by stratified flow. However, as the gas pressure increases, the initial acceleration increases, and the outburst coal mainly move forward rapidly in the form of plug flow. The average velocity at 0.3, 0.5, and 0.8 MPa gas pressure condition were 6.75, 22.22 and 35.81 m/s, respectively. Gas also has a crushing effect on outburst coal. With increasing gas pressure, the number of coal powder particles of the same mass increased significantly, and the range of the particle size distribution of the particles decreaed, and the median particle size decreased. As the gas pressure increases, the outburst intensity gradually increases, and the total energy involved in the outburst work also increases. However, the energy dissipation pathways are different. At 0.3 MPa, the energy dissipation is dominated by crushing energy, which is about six times the ejection energy. As the gas pressure increased to 0.8 MPa, the proportion of the ejection energy gradually increases to about twice that of the crushing energy. Under the experimental conditions, 2.71–13.43% of the adsorbed gas involves in the outburst (AGIO) through rapid desorption, and the proportion increases with increasing gas pressure. This paper improves the energy model of coal and gas outburst, which is applicable to risk assessment and prevention of outburst disasters. [ABSTRACT FROM AUTHOR]

As urban development accelerates and natural disasters occur more frequently, the urgency of developing effective emergency shelter planning strategies intensifies. The shelter location selection method under the traditional multi-criteria decision-making framework suffers from issues such as strong subjectivity and insufficient data support. Artificial intelligence offers a robust data-driven approach for site selection; however, many methods neglect the spatial relationships of site selection targets within geographical space. This paper introduces an emergency shelter site selection model that combines a variational graph autoencoder (VGAE) with a random forest (RF), namely VGAE-RF. In the constructed urban spatial topological graph, based on network geographic information, this model captures both the latent features of geographic unit coupling and integrates explicit and latent features to forecast the likelihood of emergency shelters in the construction area. This study takes Beijing, China, as the experimental area and evaluates the reliability of different model methods using a confusion matrix, Receiver Operating Characteristic (ROC) curve, and Imbalance Index of spatial distribution as evaluation indicators. The experimental results indicate that the proposed VGAE-RF model method, which considers spatial semantic associations, displays the best reliability. [ABSTRACT FROM AUTHOR]

A situation of emergency involving the whole population introduces changes in the dynamics of the health services that are provided. The magnitude of these shifts should be also linked to the medical speciality within which the health benefits are delivered. The aim of the paper was to identify changes in tendencies in inpatient medical service delivery during the COVID-19 pandemic by medical specialties. On the basis of a database extracted for in-patient treatment received from the public payer, a retrospective analysis was carried out. Comparing the values of the dynamics of the services provided in each medical speciality, the period before the COVID-19 pandemic was collated to the years of the pandemic (2020–2021). In the period before COVID-19, positive patient dynamics were observed in more than half of the specialities. Between 2020 and 2021, virtually all specialties reversed the trend and negative dynamics were recorded. The dynamics in 2021 indicate a process of return to the values from 2015 to 2019. Emergency situation has affected the dynamics of healthcare provision in different specialities to various extent. The most resistant to the negative impulses of the state of emergency were the areas that are strictly organisationally and financially defined (e.g. the group of "therapeutic and drug programmes"). [ABSTRACT FROM AUTHOR]

Shopping malls are crowded areas which makes the evacuation routes unable to meet personnel evacuation needs. Therefore, this paper proposes the idea of using escalators evacuation to increasing evacuation routes. In addition, the effects of escalator fire shutters on the use of escalator evacuation paths, and the efficiency of mall personnel evacuation under different conditions were simulated. The results show that the different states of fire shutters have different effects on the emergency evacuation. When the fire shutter is not lowered, it will result in 1 person not being evacuated to complete. However, when the fire shutter is lowered to 1.8 m from the ground or sprinklers are set, escalators can be used for evacuation routes are verified. And compared to the normal descent of the fire shutter (only the staircase evacuation, 2 people not evacuate completed), the evacuation of people is completed and the efficiency of evacuation is improved. This result can improve the new evacuation model for emergency evacuation plans of shopping malls with escalators. [ABSTRACT FROM AUTHOR]

Emergencies often occur irregularly, such as infectious diseases, earthquakes, wars, floods, the diffusion and leakage of chemically toxic and harmful substances, etc. These emergencies can bring huge disasters to people, even worse, the time left for people to make critical decisions is usually very limited. When an emergency occurs, the most important thing for people is to make reasonable decisions as soon as possible to deal with the current problems, otherwise, the situation may deteriorate further. The paper proposes an emergency decision-making algorithm under the constraints of the limited time and incomplete information, the research is mainly carried out from the following aspects, firstly, we use the data structure of the hesitant fuzzy probabilistic linguistic set to collect the basic data after careful comparison, which has three advantages, (1) considering the hesitation in the decision-making process, each evaluation information is allowed to contain multiple values instead of just one value; (2) each evaluation value is followed by a probability value, which further describes the details of the evaluation information; (3) the data structure allows some probability information to be unknown, which effectively expands the application scope of the algorithm. Secondly, the maximization gap model is proposed to calculate unknown parameters, the model can distinguish alternatives with small differences. Thirdly, all the evaluation information will be aggregated by the dynamic hesitant probability fuzzy weighted arithmetic operator. Subsequently, an instance is given to illustrate the effectiveness and the accuracy of the algorithm proposed in the paper. Finally, the advantages of the proposed algorithm are further demonstrated by comparing it with other outstanding algorithms. The main contribution of the paper is that we propose the maximization gap model to obtain the unknown parameters, which can effectively and accurately distinguish alternatives with small differences. [ABSTRACT FROM AUTHOR]

The stadium is a wind-sensitive structure, and the tornado has a greater damage to the stadium. The Ward tornado generating device was used to simulate the wind load characteristics of the stadium during tornado action in order to investigate the wind load characteristics of the stadium. The results of this paper are compared with the experimental results to verify the accuracy of the simulation method. The wind load characteristics of the stadium and its most vulnerable location during a tornado are ascertained by examining the wind pressure coefficient and wind speed distribution law of the stadium's upper and lower surfaces with different swirl ratio. The influence of uniform wind and wind profile on the wind load of the stadium is explored by comparing and analyzing the wind pressure coefficient of the stadium surface with and without wind profile at the entrance of the wind field. The impact of varying ground roughness on the stadium is compared in order to ascertain how it affects the wind speed, stadium wind pressure, and vortex core radius. The vorticity of the stadium in the tornado wind field with different swirl ratios was compared and analyzed, and the influence of the stadium on the vorticity development of the wind field was explored. This study reveals the effects of swirl ratio, ground roughness and wind profile on the wind pressure, wind speed and vorticity of the stadium under tornadoes, and determines the most vulnerable location and the most unfavorable factors. The above research is helpful to provide reference for disaster prevention and mitigation of stadium structure design. [ABSTRACT FROM AUTHOR]

Static formations of swarms of rotorcraft drones, used for example in disaster management, are subject to intrusions, and must bear the cost of holding the formation while avoiding collisions which leads to their increased energy consumption. While the behaviour of the intruder is unpredictable, the formation can have its parameters set to try to balance the cost of avoidance with its functionality. The novel model presented in this paper assists in the selection of parameter values. It is based on multivariate linear regression, and provides an estimate of the average disturbance caused by an intruder as a function of the values of the parameters of a formation. Cross-entropy is used as a metric for the disturbance, and the data based are generated through simulations. The model explains up to 54.4% of the variability in the value of the cross-entropy, providing results that are twice as good as the baseline estimator of the mean cross-entropy. [ABSTRACT FROM AUTHOR]

Roadways are critical infrastructure in our society, providing services for people through and between cities. However, they are prone to closures and disruptions, especially after extreme weather events like hurricanes. At the same time, traffic flow data are a fundamental type of information for any transportation system. In this paper, we tackle the problem of traffic sensor placement on roadways to address two tasks at the same time. The first task is traffic data estimation in ordinary situations, which is vital for traffic monitoring and city planning. We design a graph-based method to estimate traffic flow on roads where sensors are not present. The second one is enhanced observability of roadways in case of extreme weather events. We propose a satellite-based multi-domain risk assessment to locate roads at high risk of closures. Vegetation and flood hazards are taken into account. We formalize the problem as a search method over the network to suggest the minimum number and location of traffic sensors to place while maximizing the traffic estimation capabilities and observability of the risky areas of a city. [ABSTRACT FROM AUTHOR]

Bazhong City is located on stratum dominated by red-bed conditions. This type of weak geological condition with sand and mudstone interbedding is very easily affected by climatic conditions to produce rainfall-type landslides. On the basis of landslide data statistics collected in Bazhong City from 2011 to 2019, this paper uses ERA5-Land rainfall data to statistically analyze the correlation between rainfall and landslide events in Bazhong City. The landslide events in Bazhong City are greatly affected by rainfall events lasting for 10 days. Considering the influence of rainfall seepage and other processes, an effective cumulative rainfall-duration threshold curve for Bazhong City is obtained via median nonlinear fitting. Then, on the basis of the prediction interval, the rainfall threshold formula under different landslide occurrence probabilities is obtained and the critical threshold curve with a non-exceeding probability of 1% in Bazhong City is calculated and verified. Subsequently, a continuous probability distribution fitting function of landslide occurrence is established and a continuous probability distribution surface with a good fitting effect in Bazhong City is obtained. This allows a definite probability of whether future rainfall events will induce landslides to be obtained, providing an important basis for engineering disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Applications such as disaster management enormously benefit from rapid availability of satellite observations. Traditionally, data analysis is performed on the ground after being transferred—downlinked—to a ground station. Constraints on the downlink capabilities, both in terms of data volume and timing, therefore heavily affect the response delay of any downstream application. In this paper, we introduce RaVÆn, a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs), with the specific purpose of on-board deployment. RaVÆn pre-processes the sampled data directly on the satellite and flags changed areas to prioritise for downlink, shortening the response time. We verified the efficacy of our system on a dataset—which we release alongside this publication—composed of time series containing a catastrophic event, demonstrating that RaVÆn outperforms pixel-wise baselines. Finally, we tested our approach on resource-limited hardware for assessing computational and memory limitations, simulating deployment on real hardware. [ABSTRACT FROM AUTHOR]

Developing an effective safety emergency plan for coal mines is crucial to prevent and reduce accidents, as well as improve the emergency response capability. This paper identifies and analyzes potential accident hazards in the Burtai coal mine through a comprehensive and practical investigation of risk factors. Based on a mathematical model of a mine fire disaster relief algorithm, the MATLAB software is used to conduct numerical simulations of the dynamic spreading process of the fire smoke. Several escape routes are determined based on the simulation results, including the main escape route at the working face of coal mining, the main escape route at the working face of tunneling, and the main escape route against the reverse wind in the main inclined shaft, the auxiliary adit, and the main air intake roadway. The results presented in this study can provide guidance for improving fire emergency escape and rescue plans of the Burtai coal mine. [ABSTRACT FROM AUTHOR]

Based on an analysis of the formation mechanism, stability state and development trend, this paper focuses on monitoring key indicators, such as surface deformation and rainfall, to realize the early warning of landslides and provide some guidance for landslide disaster prevention and avoidance in the alpine mountainous area of Nangqian, Qinghai Province. The landslide of the Baima Elin Monastery has the topographic and geomorphological conditions of landslide formation and has the basic characteristics indicating that it can be influenced by rainfall and climate, so a landslide warning should be comprehensive. At the same time, the landslide is currently in an unstable state, and the seasonal freeze–thaw cycle will cause it to become more unstable. Based on macroscopic signs, it is predicted that the deformation of the Baima Elin Monastery landslide will continue to intensify without abatement, the stability will be further reduced after winter, developing in an unfavorable direction, and engineering measures should be taken as soon as possible to manage the landslide. [ABSTRACT FROM AUTHOR]

In this paper, we propose enhanced fundamental diagrams based on different water film depths by considering the effects of hydroplaning using a physical method. Various factors are calculated to describe the total safe distance headway of main vehicle components. These factors include the driver reaction times, reaction distances, vehicle braking times, and vehicle braking distances corresponding to different water film depths. An excellent match is found between the computed braking distance, the braking time calculated using the proposed numerical model, and the results published in other papers. These calculations are performed to estimate the distance headway and quantitatively analyse the relationships between the speed, density, and water film depth. By using three road-specific parameters estimated by our proposed model, namely, the free-flow speed, jam density, and capacity flow, a link transmission model is developed to analyse the dynamic impact of the water film depth. [ABSTRACT FROM AUTHOR]

Arid area is very sensitive to global warming and are extremely vulnerable to climate change. Moreover, the water resources system in the arid area is fragile and will undergo tremendous changes with climate change. Therefore, the interaction of climate and hydrology in arid area has an important impact on the formation of regional microclimate and hydrological changes. Daihai Lake is a typical closed inland lake in arid area of China, and a key area for ecological protection in North China. In this paper, WRF-Hydro model is used to simulate the climate hydrological coupling situation of Daihai Basin from 1980 to 2020, and the coupling results are verified and calibrated by meteorological statistics, runoff calculation and remote sensing analysis. Based on the synopsis of climate and hydrology in the past 40 years, the causes and future trends of the hydrological elements in Daihai Basin are analyzed. Through the analysis, it is found that the interannual variation of precipitation in Daihai Basin is sharp, with 401.75 mm as the average from 1980 to 1994; and drastic fluctuations from 1995 to 2011, with a difference of nearly 400 mm between the interannual maximum and minimum; From 2012 to 2020, the fluctuation is small. Although the interannual variation of evaporation fluctuated, it showed an upward trend with a slope of 8.855 mm/year. The annual average temperature showed an obvious upward trend with a slope of 0.040 °C/year. From 1980 to 2020, the inflow of Daihai Lake shows a downward trend; Since 2013, the runoff into the lake has tended to be flat. Climate change and human activities are the decisive factors leading to the change of water quantity in Daihai, among which human activities play a greater role. Cultivated land irrigation and industrial water use are highly correlated with the lake discharge, and these two factors have a great influence on the lake discharge. If the current agricultural and industrial water consumption does not increase, Daihai still has a lifespan of nearly 120 years. If human activities do not change and any protective measures are not taken in time, under the background of global climate change, the flow of the Daihai Lake into the lake will be reduced to zero in 2025, and the Daihai Lake will completely dry up in 2031–2033. The study of climate hydrological coupling of long time series in Daihai Basin can not only make up for the lack of runoff data, but also provide the basis for water resources management, disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Using volunteered geographic information (VGI) to supplement disaster risk management systems, including forecasting, risk assessment, and disaster recovery, is increasingly popular. This attention is driven by difficulties in detection and characterization of hazards, as well as the rise of VGI appropriate for characterizing specific forms of risk. Flash-flood historical records, especially those that are impact-based, are not comprehensive, leading to additional barriers for flash-flood research and applications. In this paper we develop a method for associating VGI flood reporting clusters against authoritative data. Using Hurricane Harvey as a case study, VGI reports are assimilated into a spatial analytic framework that derives spatial and temporal clustering parameters supported by associations between Waze's community-driven emergency operations center and authoritative reports. These parameters are then applied to find previously unreported likely flash flood-events. This study improves the understanding of the distribution of flash flooding during Hurricane Harvey and shows potential application to events in other areas where Waze data and reporting from official sources, such as the National Weather Service, are available. [ABSTRACT FROM AUTHOR]

Mapping susceptibility of landslide disaster is essential in subtropical area, where abundant rainfall may trigger landslide and mudflow, causing damages to human society. The purpose of this paper is to propose an integrated methodology to achieve such a mapping work with improved prediction results using hybrid modeling taking Chongren, Jiangxi as an example. The methodology is composed of the optimal discretization of the continuous geo-environmental factors based on entropy, weight of evidence (WoE) calculation and application of the known machine learning (ML) models, e.g., Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR). The results show the effectiveness of the proposed hybrid modeling for landslide hazard mapping in which the prediction accuracy vs the validation set reach 82.35–91.02% with an AUC [area under the receiver operating characteristic (ROC) curve] of 0.912–0.970. The RF algorithm performs best among the observed three ML algorithms and WoE-based RF modeling will be recommended for the similar landslide risk prediction elsewhere. We believe that our research can provide an operational reference for predicting the landslide hazard in the subtropical area and serve for disaster reduction and prevention action of the local governments. [ABSTRACT FROM AUTHOR]

Emergency responders require accurate and comprehensive data to make informed decisions. Moreover, the data should be acquired and analyzed swiftly to ensure an efficient response. One of the tasks at hand post-disaster is damage assessment within the impacted areas. In particular, building damage should be assessed to account for possible casualties, and displaced populations, to estimate long-term shelter capacities, and to assess the damage to services that depend on essential infrastructure (e.g. hospitals, schools, etc.). Remote sensing techniques, including satellite imagery, can be used to gathering such information so that the overall damage can be assessed. However, specific points of interest among the damaged buildings need higher resolution images and detailed information to assess the damage situation. These areas can be further assessed through unmanned aerial vehicles and 3D model reconstruction. This paper presents a multi-UAV coverage path planning method for the 3D reconstruction of postdisaster damaged buildings. The methodology has been implemented in NetLogo3D, a multi-agent model environment, and tested in a virtual built environment in Unity3D. The proposed method generates camera location points surrounding targeted damaged buildings. These camera location points are filtered to avoid collision and then sorted using the K-means or the Fuzzy C-means methods. After clustering camera location points and allocating these to each UAV unit, a route optimization process is conducted as a multiple traveling salesman problem. Final corrections are made to paths to avoid obstacles and give a resulting path for each UAV that balances the flight distance and time. The paper presents the details of the model and methodologies, and an examination of the texture resolution obtained from the proposed method and the conventional overhead flight with the nadir-looking method used in 3D mappings. The algorithm outperforms the conventional method in terms of the quality of the generated 3D model. [ABSTRACT FROM AUTHOR]

Understanding the SARS-CoV-2 dynamics has been subject of intense research in the last months. In particular, accurate modeling of lockdown effects on human behaviour and epidemic evolution is a key issue in order e.g. to inform health-care decisions on emergency management. In this regard, the compartmental and spatial models so far proposed use parametric descriptions of the contact rate, often assuming a time-invariant effect of the lockdown. In this paper we show that these assumptions may lead to erroneous evaluations on the ongoing pandemic. Thus, we develop a new class of nonparametric compartmental models able to describe how the impact of the lockdown varies in time. Our estimation strategy does not require significant Bayes prior information and exploits regularization theory. Hospitalized data are mapped into an infinite-dimensional space, hence obtaining a function which takes into account also how social distancing measures and people's growing awareness of infection's risk evolves as time progresses. This also permits to reconstruct a continuous-time profile of SARS-CoV-2 reproduction number with a resolution never reached before in the literature. When applied to data collected in Lombardy, the most affected Italian region, our model illustrates how people behaviour changed during the restrictions and its importance to contain the epidemic. Results also indicate that, at the end of the lockdown, around 12 % of people in Lombardy and 5 % in Italy was affected by SARS-CoV-2, with the fatality rate being 1.14%. Then, we discuss how the situation evolved after the end of the lockdown showing that the reproduction number dangerously increased in the summer, due to holiday relax, reaching values larger than one on August 1, 2020. Finally, we also document how Italy faced the second wave of infection in the last part of 2020. Since several countries still observe a growing epidemic and others could be subject to other waves, the proposed reproduction number tracking methodology can be of great help to health care authorities to prevent SARS-CoV-2 diffusion or to assess the impact of lockdown restrictions on human behaviour to contain the spread. [ABSTRACT FROM AUTHOR]

Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average R 2 of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model's interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively. [ABSTRACT FROM AUTHOR]

Oral and maxillofacial trauma is influenced by various factors, including regional characteristics and social background. Due to the coronavirus disease 2019 (COVID-19) pandemic, a state of emergency was declared in Japan in March 2020. In this study, we aimed to examine the dynamics of patients with oral and maxillofacial trauma over a 12-years period using interrupted time-series (ITS) analysis. Patients were examined at the Shimane University Hospital, Maxillofacial Trauma Center from April 2012 to April 2023. In addition to general patient characteristics, data regarding the type of trauma and its treatment were obtained from 1203 patients (770 men and 433 women). Group comparisons showed significant differences in age, trauma status, method of treatment, referral source, route, and injury occasion. ITS analysis indicated significant changes in combined nasal fractures, non-invasive reduction, and sports injuries (P < 0.05), suggesting COVID-19 significantly impacted oral and maxillofacial trauma dynamics. A pandemic of an infectious disease may decrease the number of minor trauma cases but increase the number of injuries from outdoor activities, resulting in no overall change in the dynamics of the number of trauma patients. Medical systems for oral and maxillofacial trauma should be in place at all times, independent of infectious disease pandemics. [ABSTRACT FROM AUTHOR]

The instability of slope has already threatened life and property safety of the people, and improving the monitoring method of slope stability has important theoretical and practical significance for disaster prevention and reduction. According to the idea of "Newton force sudden drop and catastrophic occurrence" proposed by M.C. He in the landslide monitoring, a numerical model with constant resistance bolt has been established. Through numerical simulation research, it is found that the maximum principal stress, minimum principal stress and shear stress of the intersection point P of landslide surface and constant resistance bolt are sudden growth and sudden decrease, the vertical displacement and lateral displacement of this point P appear rise and fall before three kinds of stress. When loading to the next step of the step where three stress have reduced to a minimum value the slope is unstable and destroyed. At this time, the constant resistance bolt has undergone larger plastic deformation and damaged. Finally, comparing the stress curves and the acoustic emission (AE) curves, it can be seen that stress decreases from the maximum value and the AE curves begin to show a significant rise, the two curves display opposite law. It can be seen from the AE diagram that the failure mode of the slope is a combined tension and shear failure. The numerical test results provide a new idea for real-time monitoring and forecasting of slope. [ABSTRACT FROM AUTHOR]

Hail, a highly destructive weather phenomenon, necessitates critical identification and forecasting for the protection of human lives and properties. The identification and forecasting of hail are vital for ensuring human safety and safeguarding assets. This research proposes a deep learning algorithm named Dual Attention Module EfficientNet (DAM-EfficientNet), based on EfficientNet, for detecting hail weather conditions. DAM-EfficientNet was evaluated using FY-4A satellite imagery and real hail fall records, achieving an accuracy of 98.53% in hail detection, a 97.92% probability of detection, a false alarm rate of 2.08%, and a critical success index of 95.92%. DAM-EfficientNet outperforms existing deep learning models in terms of accuracy and detection capability, with fewer parameters and computational needs. The results validate DAM-EfficientNet's effectiveness and superior performance in hail weather detection. Case studies indicate that the model can accurately forecast potential hail-affected areas and times. Overall, the DAM-EfficientNet model proves to be effective in identifying hail weather, offering robust support for weather disaster alerts and prevention. It holds promise for further enhancements and broader application across more data sources and meteorological parameters, thereby increasing the precision and timeliness of hail forecasting to combat hail disasters and boost public safety. [ABSTRACT FROM AUTHOR]

The scale of volcanic eruptions influences the effects of wide-ranging disasters involving ashfall, such as the disruption of lifelines and paralysis of urban functions. This highlights the importance of vehicle availability on ash-covered roads for disaster management personnel involved in rescue and recovery efforts and for citizens who must evacuate or continue their social lives. We conducted tests to scientifically verify the running ability of vehicles on ash-covered roads. Results revealed that all-wheel-drive vehicles showed better running performance than two-wheel-drive vehicles, which get stuck when ashfall thickness exceeds 10 cm. Most of the vehicle's drive power is consumed as energy to scrape ash grains from under the tires, hindering sufficient propulsion. In addition, the tires sink into the ash layer, which increases driving resistance and causes the vehicle to get stuck. Running ability on ash-covered roads is mainly determined by the relation between the "drive system" of the vehicle and the "thickness of ash" on the roads. In addition, road surface conditions, including ash thickness, could change in time and space because of traffic volume and weather conditions. [ABSTRACT FROM AUTHOR]

Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. Solar flares and accompanied coronal mass ejections are sources of space weather, which negatively affects a variety of technologies at or near Earth, ranging from blocking high-frequency radio waves used for radio communication to degrading power grid operations. Monitoring and providing early and accurate prediction of solar flares is therefore crucial for preparedness and disaster risk management. In this article, we present a transformer-based framework, named SolarFlareNet, for predicting whether an AR would produce a γ -class flare within the next 24 to 72 h. We consider three γ classes, namely the ≥ M5.0 class, the ≥ M class and the ≥ C class, and build three transformers separately, each corresponding to a γ class. Each transformer is used to make predictions of its corresponding γ -class flares. The crux of our approach is to model data samples in an AR as time series and to use transformers to capture the temporal dynamics of the data samples. Each data sample consists of magnetic parameters taken from Space-weather HMI Active Region Patches (SHARP) and related data products. We survey flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and build a database of flares with identified ARs in the NCEI flare catalogs. This flare database is used to construct labels of the data samples suitable for machine learning. We further extend the deterministic approach to a calibration-based probabilistic forecasting method. The SolarFlareNet system is fully operational and is capable of making near real-time predictions of solar flares on the Web. [ABSTRACT FROM AUTHOR]

In the course of underground engineering, layered slate is often encountered. Understanding the mechanical characteristics of layered slate is a prerequisite for engineering construction and disaster prevention and mitigation. As a result, at the beginning of a project, a large number of indoor tests are required, which are time-consuming and laborious. In addition, the natural joint angle of the layered slate is random, so it is extremely difficult to establish a database of the mechanical characteristics of layered slate. Hence, it is necessary to find a simple, feasible, and high-precision method to determine the Holmquist–Johnson–Cook (HJC) constitutive model parameters for naturally jointed layered slate with different dip angles. This study first determines the HJC constitutive model parameters of layered slate with five specific joint dip angles (0°, 30°, 45°, 60°, and 90°) through static tests and the split Hopkinson pressure bar (SHPB) test. Furthermore, by employing sensitivity analysis methods, the influence of key parameters of the HJC constitutive model on the dynamic peak stress of slate is determined. Among them, parameters A and B have the most significant impact on stress, exceeding 50%. Thirdly, a nonlinear fitting regression method is used to determine the HJC constitutive model parameters of naturally jointed angular slate. The relationship between the HJC model parameters and the inclination angle of slate joints is derived, and the accuracy of these parameters is verified through numerical simulation methods. The error between the numerical simulation and indoor experiments is within 10%, indicating a high level of simulation accuracy. The research findings provide a highly precise numerical simulation method for similar projects. [ABSTRACT FROM AUTHOR]

A deep knowledge of the failure mechanisms and early warning of dangerous rocks is an important issue in geological disaster prevention and reduction. This study focuses on the failure analysis of dangerous rocks from a laboratory scale, whose models are prepared by 3D printing (3DP) technology. The frozen–thawing test (FTT) is performed to reproduce the failure processes of toppling and falling types dangerous rocks. In addition, the digital image correlation (DIC) technique is applied to detect the deformation characteristics of dangerous rock models during the tests. The relative displacements along the structural plane and the displacement vectors on the dangerous rock surface are further extracted to quantitatively reveal the failure mechanism from a fine-view perspective. It is found that the toppling type dangerous rock is dominated by the rotational failure, while the falling type dangerous rock is dominated by tensile‒shear failure. Furthermore, a DIC-based early warning method is proposed for identifying the precursors of dangerous rock instability from a laboratory perspective. The results provide an important application and reference value for the study of dangerous rock prevention and reduction. [ABSTRACT FROM AUTHOR]

The overlying strata of the lower coal seam is easy to be collapsed causing the roof caving accident at the end face of the mining working face under repeated mining in close-distance coal seams. In order to predict the roof instability of the end face, the mechanical model of the granular arch structure is established in this study to further analyze its main influencing factors. The results show that the mining height of the working face, the advancing speed, the distance of coal seams, the tip-to-face distance, the strength of the surrounding rock and the support setting the load of the support are the main influencing factors on the roof caving of the end face. Subsequently, the prediction model of roof instability in the end face under repeated mining is constructed through the radial basis function neural network (RBFNN) and the above main influencing factors are regarded as input layer indexes. Meanwhile, the roof subsidence, coal wall deformation and support load are determined as the output layer indexes. The predicted results are closer to the results of sample tests. Finally, the early warning system, including monitoring and early warning, data query, emergency management, user management, and system settings, is designed to monitor roof conditions of the end face and timely warn the roof accidents. The field application proves that the system has good practical value, which is of great significance to intelligent prediction of coal mine stope disaster and prevent the end face roof disaster under repeated mining and. This will promote the safe and efficient construction of coal mine production. [ABSTRACT FROM AUTHOR]

Due to the frequent occurrence of numerous emergency events that have significantly damaged society and the economy, the need for emergency decision-making has been manifest recently. It assumes a controllable function when it is critical to limit property and personal catastrophes and lessen their negative consequences on the natural and social course of events. In emergency decision-making problems, the aggregation method is crucial, especially when there are more competing criteria. Based on these factors, we first introduced some basic concepts about SHFSS, and then we introduced some new aggregation operators such as the spherical hesitant fuzzy soft weighted average, spherical hesitant fuzzy soft ordered weighted average, spherical hesitant fuzzy weighted geometric aggregation, spherical hesitant fuzzy soft ordered weighted geometric aggregation, spherical hesitant fuzzy soft hybrid average, and spherical hesitant fuzzy soft hybrid geometric aggregation operator. The characteristics of these operators are also thoroughly covered. Also, an algorithm is developed within the spherical hesitant fuzzy soft environment. Furthermore, we extend our investigation to the Evaluation based on the Distance from Average Solution method in multiple attribute group decision-making with spherical hesitant fuzzy soft averaging operators. And a numerical illustration for "supply of emergency aid in post-flooding the situation" is given to show the accuracy of the mentioned work. Then a comparison between these operators and the EDAS method is also established in order to further highlight the superiority of the established work. [ABSTRACT FROM AUTHOR]

Shanghai is susceptible to land subsidence due to its unique geological environment and frequent human activities. Traditional leveling techniques are not sufficient for monitoring large areas of land subsidence due to the time-consuming, labor-intensive, and expensive nature of the process. Furthermore, the results of conventional methods may not be timely, rendering them ineffective for monitoring purposes. Interferometric Synthetic Aperture Radar (InSAR) technology is a widely used method for monitoring ground subsidence due to its low cost, high efficiency, and ability to cover large areas. To monitor the surface sink condition of Shanghai over the past 2 years, monitoring data were obtained through the technical processing of 24 images from Sentinel-1A data covering Shanghai from 2019 to 2020 using the Persistent Scatterer (PS-InSAR) and Small Baseline Subset (SBAS-InSAR) technique. The ground subsidence (GS) results were extracted via PS and SBAS interferometry processing, while Shuttle Radar Topography Mission data were used to correct the residual phase. According to PS and SBAS methods, the maximum ground subsidence in the study area reached 99.8 mm and 47.2 mm, respectively. The subsidence rate and the accumulated amount of subsidence derived from the monitoring results revealed the urban area in Shanghai to be principally characterized by uneven GS, with multiple settlement funnels being found to be distributed across the main urban area. Moreover, when compared with the historical subsidence data, geological data, and urban construction distribution data, the individual settlement funnels were observed to correspond to those data concerning the historical surface settlement funnel in Shanghai. By randomly selecting GS time-series data regarding three feature points, it was determined that the morphological variables of the GS remained largely consistent at all time points and that their change trends exhibited a high degree of consistency, which verified the reliability of the PS-InSAR and SBAS-InSAR monitoring method. The results can provide data support for decision making in terms of geological disaster prevention and control in Shanghai. [ABSTRACT FROM AUTHOR]

Emergency capability assessment is a complex system with multiple factors, variables and levels. Incomplete and uncertain assess information often occurs during assessment. Based on this, a method combining D-number theory and fuzzy analytic Hierarchy process (FAHP) is proposed to study the emergency capacity of coal enterprises in Longdong area. On the basis of analyzing the limitation of D-S evidence theory, the D-number theory was optimized and improved. According to the principles of systematicness, feasibility, scientificity and timeliness, a hierarchical structure model of enterprise emergency capability assessment was constructed from the perspective of pre-incident, mid-incident and post-incident, which consisted of 4 first-level indicators and 18 s-level indicators. The weight and importance of the assessment index of emergency response capability are calculated by organically integrating the D-number preference relation with the hierarchy structure. Combined with the assessment results of experts, a quantitative analysis and evaluation of the emergency response capacity of a coal enterprise was conducted by using FAHP. The comprehensive score of the enterprise's emergency response capability was 80.45, and the level of emergency response capacity was "good". The research results show that the D-FAHP method has high reliability in evaluating the emergency response capability of coal enterprises, avoiding the impact of uncertain and incomplete information on the assessment results. This can not only effectively identify the weak links in emergency management, but also meet the emergency decision-making needs of enterprises in the emergency state, which has important guiding significance to improve the ability and level of enterprise emergency management. [ABSTRACT FROM AUTHOR]

This study aims at increasing earthquake preparedness knowledge perception and practice among Nepalese immigrants residing in Japan through an educational intervention. A single arm quasi experimental study was conducted among Nepalese immigrants residing in Japan. An educational intervention was prepared along with a 52 itemed questionnaire. In total, 165 participants responded to our questionnaire. Majority of them were male (67.88%), and the mean age was 32.78 years. Generalized equation model showed that the knowledge score of earthquake preparedness was 4.01 points higher immediately after the intervention [95% CI (2.78–5.24), p-value < 0.001] compared to baseline with a further increase by 7.02 points [95% CI (5.96–8.09), p-value < 0.001] at two weeks follow up. However, the practice score increased only by 2.83 points [95% CI (2.51–3.14), p-value < 0.001] immediately after the intervention with a similar increase at two weeks and 12 weeks follow up period [OR: 2.62, 95% CI (2.29–2.96), p-value < 0.001]. The educational intervention, when conducted in native language, can increase both the knowledge and practice score of earthquake preparedness hence, information related to earthquake preparedness in Nepali languages in the government websites of Japan could potentially increase information seeking behavior of people. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System-Acoustic ranging combination technique (GNSS-A) is the only geodetic observation method that can precisely detect absolute horizontal and vertical seafloor crustal deformations at the centimetre scale. GNSS-A has detected many geophysical phenomena and is expected to make great contributions to earthquake disaster prevention science and geodesy. However, current observation methods that use vessels and buoys suffer from high cost or poor real-time performance, which leads to low observation frequency and delays in obtaining and transmitting disaster prevention information. To overcome these problems, a new sea surface platform is needed. Here, we present an unmanned aerial vehicle (UAV) system developed for GNSS-A surveys capable of landing on the sea surface. Submetre-level seafloor positioning is achieved based on real-time single-frequency GNSS data acquired over an actual site. UAV-based GNSS-A allows high-frequency, near real-time deployment, and low-cost seafloor geodetic observations. This system could be deployed to acquire high-frequency observations with centimetre-scale accuracies when using dual-frequency GNSS. [ABSTRACT FROM AUTHOR]

Landslides are the most frequent and diffuse natural hazards in Italy causing the greatest number of fatalities and damage to urban areas. The integration of natural hazard information and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. The news about landslide events in newspapers or crowdsourcing platforms allows fast observation, surveying and classification. Currently, few studies have been produced on the combination of social media data and traditional sensors. This gap indicates that it is unclear how their integration can effectively provide emergency managers with appropriate knowledge. In this work, rainfall, human lives, and earmarked fund data sources were correlated to "landslide news". Analysis was applied to obtain information about temporal (2010–2019) and spatial (regional and warning hydrological zone scale) distribution. The temporal distribution of the data shows a continuous increase from 2015 until 2019 for both landslide and rainfall events. The number of people involved and the amount of earmarked funds do not exhibit any clear trend. The spatial distribution displays good correlation between "landslide news", traditional sensors (e.g., pluviometers) and possible effects in term of fatalities. In addition, the cost of soil protection, in monetary terms, indicates the effects of events. [ABSTRACT FROM AUTHOR]

Water distribution systems (WDSs) are used to transmit and distribute water resources in cities. Water distribution networks (WDNs) are partitioned into district metered areas (DMAs) by water network partitioning (WNP), which can be used for leak control, pollution monitoring, and pressure optimization in WDS management. In order to overcome the limitations of optimal search range and the decrease of recovery ability caused by two-step WNP and fixed DMAs in previous studies, this study developed a new method combining a graph neural network to realize integrated WNP and dynamic DMAs to optimize WDS management and respond to emergencies. The proposed method was tested in a practical case study; the results showed that good hydraulic performance of the WDN was maintained and that dynamic DMAs demonstrated excellent stability in emergency situations, which proves the effectiveness of the method in WNP. [ABSTRACT FROM AUTHOR]

Landslide disasters cause huge casualties and economic losses every year, how to accurately forecast the landslides has always been an important issue in geo-environment research. In this paper, a hybrid machine learning approach RSLMT is firstly proposed by coupling Random Subspace (RS) and Logistic Model Tree (LMT) for producing a landslide susceptibility map (LSM). With this method, the uncertainty introduced by input features is considered, the problem of overfitting is solved by reducing dimensions to increase the prediction rate of landslide occurrence. Moreover, the uncertainty of prediction will be deeply discussed with the rank probability score (RPS) series, which is an important evaluation of uncertainty but rarely used in LSM. Qingchuan county, China was taken as a study area. 12 landslide causal factors were selected and their contribution on landslide occurrence was evaluated by ReliefF method. In addition, Logistic Model Tree (LMT), Naive Bayes (NB) and Logistic Regression (LR) were researched for comparison. The results showed that RSLMT (AUC = 0.815) outperformed LMT (AUC = 0.805), NB (AUC = 0.771), LR (AUC = 0.785). LSM of Qingchuan county was produced using the novel model, it indicated that landslides tend to occur along with the fault belts and the middle-low mountain area that is strongly influenced by the large numbers of human engineering activities. [ABSTRACT FROM AUTHOR]

To prepare for upcoming extreme events, decision makers, scientists and other stakeholders require a thorough understanding of the vulnerability of the built environment to natural hazards. A vulnerability index based on building characteristics (indicators) rather than empirical data may be an alternative approach to a comprehensive physical vulnerability assessment of the building stock. The present paper focuses on the making of such an index for dynamic flooding in mountain areas demonstrating the transferability of vulnerability assessment approaches between hazard types, reducing the amount of required data and offering a tool that can be used in areas were empirical data are not available. We use data from systematically documented torrential events in the European Alps to select and weight the important indicators using an all-relevant feature selection algorithm based on random forests. The permutation-based feature selection reduced the initial number of indicators from 22 to seven, decreasing in this way the amount of required data for assessing physical vulnerability and ensuring that only relevant indicators are considered. The new Physical Vulnerability Index (PVI) may be used in the mountain areas of Europe and beyond where only few empirical data are available supporting decision-making in reducing risk to dynamic flooding. [ABSTRACT FROM AUTHOR]

Floods are the most commonly occurring natural disasters in India due to India's unique geographical location and socioeconomic conditions. Frequent flooding causes enormous loss of human lives and damage crops and public utilities. Furthermore, floods adversely affect economic development and increase the government's financial burden by increasing spending on various disaster mitigation measures. Recent empirical literature based on cross-national comparisons shows that disaster fatalities and damages are monotonically decreasing in per capita income. We challenge this view on the monotonic negative relationship between income and flood damages. We examine the non-monotonic (inverted U-shaped) relationship between per capita income and flood impact in terms of deaths, people affected, and damages due to floods in 19 major Indian states from 1980 to 2011, using Poisson and Tobit estimation methods. In particular, deaths and the population affected by floods increase with a turning point of income up to 882 US$ and 578 US$, respectively, and diminishes thereafter. Our results confirm an inverted U-shaped relationship between income and fatalities and the population affected by floods. In addition to income, we argue that government responsiveness plays an essential role in mitigating the risk of floods. We employ the fixed-effect Poisson estimation method to examine the government's role in protecting people against disaster risk, focusing on regional differences in India. Deaths from floods remain non-linear and follow the inverted U-pattern with respect to government responsiveness. However, the effect of government responsiveness on flood fatalities and flood damages is statistically insignificant. Our results further suggest that high-income states experience a lower death toll from floods. The high-income (rich) states are capable of incurring a higher threshold level of income and higher natural calamity expenditure to reduce flood fatalities and protect the population affected by floods than the low-income (poor) states. The poor states have minimal resources and face severe financial constraints to reduce the death toll from floods. From the perspective of public policy, the poor states, in particular, require an increase in income, better governance, and effective disaster management policies to mitigate flood impact. [ABSTRACT FROM AUTHOR]

Social media platforms have proved to be vital sources of information to support disaster response and recovery. A key issue, though, is that social media conversation about disasters tends to tail off after the immediate disaster response phase, potentially limiting the extent to which social media can be relied on to support recovery. This situation motivates the present study of social media usage patterns, including who contributes to social media around disaster recovery, which recovery activities they contribute to, and how well that participation is sustained over time. Utilising Twitter data from the 2019–20 Australian bushfires, we statistically examined the participation of different groups (citizens, emergency agencies, politicians and others) across categories of disaster recovery activity such as donations & financial support or mental health & emotional support, and observed variations over time. The results showed that user groups differed in how much they contributed on Twitter around different recovery activities, and their levels of participation varied with time. Recovery-related topics also varied significantly with time. These findings are valuable because they increase our understanding of which aspects of disaster recovery currently benefit most from social media and which are relatively neglected, indicating where to focus resources and recovery effort. [ABSTRACT FROM AUTHOR]

When a severe natural disaster occurs, the extraction of post-disaster building damage information is one of the methods to quickly obtain disaster information. The increasingly mature high-resolution remote sensing technology provides a solid foundation for obtaining information about building damage. To address the issues with inaccurate building positioning in existing building damage assessment methods, as well as poor classification due to similar minor and major damage characteristics in building damage classification. Based on U-Net, we designed a two-stage building damage assessment network. The first stage is an independent U-Net focused on building segmentation, followed by a Siamese U-Net focused on building damage classification. The Extra Skip Connection and Asymmetric Convolution Block were used for enhancing the network's ability to segment buildings on different scales; Shuffle Attention directed the network's attention to the correlation of buildings before and after the disaster. The xBD dataset was used for training and testing in the study, and the overall performance was evaluated using a balanced F-score (F1). The improved network had an F1 of 0.8741 for localization and F1 of 0.7536 for classification. When compared to other methods, it achieved better overall performance for building damage assessment and was able to generalize to multiple disasters. [ABSTRACT FROM AUTHOR]

Due to the mixed distribution of buildings and vegetation, wildland-urban interface (WUI) areas are characterized by complex fuel distributions and geographical environments. The behavior of wildfires occurring in the WUI often leads to severe hazards and significant damage to man-made structures. Therefore, WUI areas warrant more attention during the wildfire season. Due to the ever-changing dynamic nature of California's population and housing, the update frequency and resolution of WUI maps that are currently used can no longer meet the needs and challenges of wildfire management and resource allocation for suppression and mitigation efforts. Recent developments in remote sensing technology and data analysis algorithms pose new opportunities for improving WUI mapping methods. WUI areas in California were directly mapped using building footprints extracted from remote sensing data by Microsoft along with the fuel vegetation cover from the LANDFIRE dataset in this study. To accommodate the new type of datasets, we developed a threshold criteria for mapping WUI based on statistical analysis, as opposed to using more ad-hoc criteria as used in previous mapping approaches. This method removes the reliance on census data in WUI mapping, and does not require the calculation of housing density. Moreover, this approach designates the adjacent areas of each building with large and dense parcels of vegetation as WUI, which can not only refine the scope and resolution of the WUI areas to individual buildings, but also avoids zoning issues and uncertainties in housing density calculation. Besides, the new method has the capability of updating the WUI map in real-time according to the operational needs. Therefore, this method is suitable for local governments to map local WUI areas, as well as formulating detailed wildfire emergency plans, evacuation routes, and management measures. [ABSTRACT FROM AUTHOR]

Out-of-hospital cardiac arrest (OHCA) is a worldwide health problem. The aim of the study is to utilize the territorial-wide OHCA data of Hong Kong in 2012–2015 to examine its spatiotemporal pattern and high-risk neighborhoods. Three techniques for spatiotemporal data mining (SaTScan's spatial scan statistic, Local Moran's I, and Getis Ord Gi*) were used to extract high-risk neighborhoods of OHCA occurrence and identify local clusters/hotspots. By capitalizing on the strengths of these methods, the results were then triangulated to reveal "truly" high-risk OHCA clusters. The final clusters for all ages and the elderly 65+ groups exhibited relatively similar patterns. All ages groups were mainly distributed in the urbanized neighborhoods throughout Kowloon. More diverse distribution primarily in less accessible areas was observed among the elderly group. All outcomes were further converted into an index for easy interpretation by the general public. Noticing the spatial mismatches between hospitals and ambulance depots (representing supplies) and high-risk neighborhoods (representing demands), this setback should be addressed along with public education and strategic ambulance deployment plan to shorten response time and improve OHCA survival rate. This study offers policymakers and EMS providers essential spatial evidence to assist with emergency healthcare planning and informed decision-making. [ABSTRACT FROM AUTHOR]

A multi-attribute group decision-making (MAGDM) method based on intuitionistic fuzzy preference information is proposed for the multi-attribute intuitionistic fuzzy group decision-making problem where the decision-makers weight and attribute weight are completely unknown and the decision-maker has preference information for the scheme. Firstly, an intuitionistic fuzzy interval judgment matrix is established to describe the original data of the key decision indicators for multiple network public opinion emergencies that erupt simultaneously. Secondly, the attribute weights are determined based on the improved intuitionistic fuzzy entropy construction method, and the expert weights are determined by using objective decision information, taking into account the intuitionistic fuzzy entropy of decision matrix. MAGDM can not only synthesize experts' professional experience in different aspects, but also avoid experts' decision-making errors caused by insufficient domain knowledge. It is necessary to continuously adjust the expert weight based on decision-making information to make the comprehensive decision-making information more accurate. Thirdly, a scheme preference model and an attribute weight optimization model are established to determine the ranking method of intuitionistic fuzzy interval values. Then, a modified distance measure of intuitionistic fuzzy sets (IFSs) is introduced to make the evaluation result more accurate and reasonable when it comes to solving the deviation between the evaluation value and ideal solution of each scheme. Finally, the effectiveness and practicability of the proposed decision-making method are verified by an example of emergency crisis severity, It assists decision makers in selecting and implementing the optimal emergency response plan in a timely and efficient manner, which improves the emergency treatment efficiency of network public opinion crisis, helps emergency departments to better deal with the network public opinion crisis, improves the ability of public opinion guidance and control, and provides a new method and idea for multi-attribute intuitionistic fuzzy group decision-making problem. [ABSTRACT FROM AUTHOR]

Large magnitude earthquakes produce complex surface deformations, which are typically mapped by field geologists within the months following the mainshock. We present detailed maps of the surface deformation pattern produced by the M. Vettore Fault System during the October 2016 earthquakes in central Italy, derived from ALOS-2 SAR data, via DInSAR technique. On these maps, we trace a set of cross-sections to analyse the coseismic vertical displacement, essential to identify both surface fault ruptures and off-fault deformations. At a local scale, we identify a large number of surface ruptures, in agreement with those observed in the field. At a larger scale, the inferred coseismic deformation shows a typical long-wavelength convex curvature of the subsiding block, not directly recognizable in the field. The detection of deformation patterns from DInSAR technique can furnish important constraints on the activated fault segments, their spatial distribution and interaction soon after the seismic events. Thanks to the large availability of satellite SAR acquisitions, the proposed methodological approach can be potentially applied to worldwide earthquakes (according to the environmental characteristics of the sensed scene) to provide a wider and faster picture of surface ruptures. Thus, the derived information can be crucial for emergency management by civil protection and helpful to drive and support the geological field surveys during an ongoing seismic crisis. [ABSTRACT FROM AUTHOR]

Although tsunamis are dispersive water waves, hazard maps for earthquake-generated tsunamis neglect dispersive effects because the spatial dimensions of tsunamis are much greater than the water depth, and dispersive effects are generally small. Furthermore, calculations that include non-dispersive effects tend to predict higher tsunamis than ones that include dispersive effects. Although non-dispersive models may overestimate the tsunami height, this conservative approach is acceptable in disaster management, where the goal is to save lives and protect property. However, we demonstrate that offshore frequency dispersion amplifies tsunamis caused by outer-rise earthquakes, which displace the ocean bottom downward in a narrow area, generating a dispersive short-wavelength and pulling-dominant (water withdrawn) tsunami. We compared observational evidence and calculations of tsunami for a 1933 Mw 8.3 outer-rise earthquake along the Japan Trench. Dispersive (Boussinesq) calculations predicted significant frequency dispersion in the 1933 tsunami. The dispersive tsunami deformation offshore produced tsunami inundation heights that were about 10% larger than those predicted by non-dispersive (long-wave) calculations. The dispersive tsunami calculations simulated the observed tsunami inundation heights better than did the non-dispersive tsunami calculations. Contrary to conventional practice, we conclude that dispersive calculations are essential when preparing deterministic hazard maps for outer-rise tsunamis. [ABSTRACT FROM AUTHOR]

The selection of optimal search effort for air-sea integrated search has become the most concerned issue for maritime search and rescue (MSAR) departments. Helicopters play an important role in maritime search because of their strong maneuverability and hovering ability. In this work, the requirements of maritime search were analyzed, from which a global optimization model with quantitative constraints for vessels and aircraft was developed by setting the least search time as single-objective optimization problem; then the improved Dinkelbach algorithm was used to solve the continuous programming problem, and the discrete mission planning algorithm was used to improve the calculation accuracy of search time and area. A case study shows that the errors in calculating search time and area decrease from 12–18 min to 36 s and from 76.5 to 0.45 n mile2, respectively. The results obtained from the discrete mission planning algorithm can provide better guidance for MASR departments in selecting optimal search scheme. [ABSTRACT FROM AUTHOR]

In the early stages of an outbreak, the term 'pandemic' can be used to communicate about infectious disease risk, particularly by those who wish to encourage a large-scale public health response. However, the term lacks a widely accepted quantitative definition. We show that, under alternate quantitative definitions of 'pandemic', an epidemiological metapopulation model produces different estimates of the probability of a pandemic. Critically, we show that using different definitions alters the projected effects of key parameters—such as inter-regional travel rates, degree of pre-existing immunity, and heterogeneity in transmission rates between regions—on the risk of a pandemic. Our analysis provides a foundation for understanding the scientific importance of precise language when discussing pandemic risk, illustrating how alternative definitions affect the conclusions of modelling studies. This serves to highlight that those working on pandemic preparedness must remain alert to the variability in the use of the term 'pandemic', and provide specific quantitative definitions when undertaking one of the types of analysis that we show to be sensitive to the pandemic definition. [ABSTRACT FROM AUTHOR]

In this study, we propose a contagion model as a simple and powerful mathematical approach for predicting the spatial spread and temporal evolution of the onset and recession of floodwaters in urban road networks. A network of urban roads resilient to flooding events is essential for the provision of public services and for emergency response. The spread of floodwaters in urban networks is a complex spatial–temporal phenomenon. This study presents a mathematical contagion model to describe the spatial–temporal spread and recession process of floodwaters in urban road networks. The evolution of floods within networks can be captured based on three macroscopic characteristics—flood propagation rate (β ), flood incubation rate (α ), and recovery rate (μ )—in a system of ordinary differential equations analogous to the Susceptible-Exposed-Infected-Recovered (SEIR) model. We integrated the flood contagion model with the network percolation process in which the probability of flooding of a road segment depends on the degree to which the nearby road segments are flooded. The application of the proposed model is verified using high-resolution historical data of road flooding in Harris County during Hurricane Harvey in 2017. The results show that the model can monitor and predict the fraction of flooded roads over time. Additionally, the proposed model can achieve 90% precision and recall for the spatial spread of the flooded roads at the majority of tested time intervals. The findings suggest that the proposed mathematical contagion model offers great potential to support emergency managers, public officials, citizens, first responders, and other decision-makers for flood forecast in road networks. [ABSTRACT FROM AUTHOR]

Literature on earthquake impact on hospital admissions is lacking, particularly in low-resource settings. Our aim was to study the pattern of admissions before and after the 2015 earthquake in a tertiary hospital in Nepal. We used routine hospital data from 9,596 admissions, and defined four periods: pre-earthquake (pre-EQ), acute (EQ1), post-acute (EQ2), and post-earthquake (post-EQ). We compared length of hospital stay (LOS) across the study periods using negative binomial regressions. We used logistic regressions to study changes in probability of admission for diagnostic categories, and Generalized Additive Models to model the difference in number of admissions compared to pre-EQ baseline. LOS was longer in EQ1 than during pre-EQ, in particular for injury-related admissions. In EQ1, the odds of injury admissions increased, while they decreased for the majority of other diagnoses, with the odds of pregnancy-related admissions remaining low until post-EQ. The number of admissions dropped in EQ1 and EQ2, and returned to pre-EQ trends in post-EQ, accumulating 381 admissions lost (CI: 206–556). Our findings suggest that hospital disaster plans must not only foresee injury management after earthquakes, but also ensure accessibility, in particular for pregnant women, and promote a quick return to normality to prevent additional negative health outcomes. [ABSTRACT FROM AUTHOR]

Delay embedding—a method for reconstructing dynamical systems by delay coordinates—is widely used to forecast nonlinear time series as a model-free approach. When multivariate time series are observed, several existing frameworks can be applied to yield a single forecast combining multiple forecasts derived from various embeddings. However, the performance of these frameworks is not always satisfactory because they randomly select embeddings or use brute force and do not consider the diversity of the embeddings to combine. Herein, we develop a forecasting framework that overcomes these existing problems. The framework exploits various "suboptimal embeddings" obtained by minimizing the in-sample error via combinatorial optimization. The framework achieves the best results among existing frameworks for sample toy datasets and a real-world flood dataset. We show that the framework is applicable to a wide range of data lengths and dimensions. Therefore, the framework can be applied to various fields such as neuroscience, ecology, finance, fluid dynamics, weather, and disaster prevention. [ABSTRACT FROM AUTHOR]

We examined whether pre-disaster social support functions as a disaster preparedness resource to mitigate post-disaster depressive symptoms among older survivors of the 2011 Great East Japan earthquake and tsunami. The participants were 3,567 individuals aged ≥65 years living in Iwanuma city who completed a baseline survey as part of the nationwide Japan Gerontological Evaluation Study seven months before the disaster. A follow-up survey was administered approximately 2.5 years after the disaster. The analysis included a total of 2,293 participants, and social support (giving and receiving emotional & instrumental help) before the disaster was measured using four items. Depressive symptoms were assessed using the GDS with a cut-off score of 4/5 (not depressed/depressed). We discovered that participants who gave and received emotional and instrumental support before the disaster were significantly less likely to develop depressive symptoms after the disaster compared to those without support (ARR = 0.70; 95% CI: 0.56–0.88). The risk of the onset of depressive symptoms was 1.34 (95% CI: 1.03–1.74) among those who experienced disaster damages but had also given and received social support, and 1.70 (95% CI: 1.03–2.76) among those who experienced damages but lacked support. Strengthening social aid may help cultivate psychological resilience to disasters. [ABSTRACT FROM AUTHOR]