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Against the backdrop of increasingly severe global climate change, the risk of rainstorm-induced waterlogging has become the primary threat to the safety of historic and cultural districts worldwide. This paper focuses on the historic and cultural districts of Beijing, China, and explores techniques and methods for identifying extreme rainstorm warnings in cultural heritage areas. Refined warning and forecasting have become important non-engineering measures to enhance these districts' waterlogging prevention control and emergency management capabilities. This paper constructs a rainstorm-induced waterlogging risk warning model tailored for Beijing's historical and cultural districts. This model system encompasses three sets of models: a building waterlogging early-warning model, a road waterlogging early-warning model, and a public evacuation early-warning model. During the construction of the model, the core concepts and determination methods of "1 h rainfall intensity water logging index" and "the waterlogging risk index in historical and cultural districts" were proposed. The construction and application of the three models take into full account the correlation between rainfall intensity and rainwater accumulation, while incorporating the characteristics of flood resilience in buildings, roads, and the society in districts. This allows for a precise grading of warning levels, leading to the formulation of corresponding warning response measures. Empirical tests have shown that the construction method proposed in this paper is reliable. The innovative results not only provide a new perspective and method for the early-warning of rainstorm-induced waterlogging, but also offer scientific support for emergency planning and response in historical and cultural districts. [ABSTRACT FROM AUTHOR]

Under the background of global climate change, rainstorm and flood disasters have become the most serious cataclysm. Under the circumstances of an increasingly severe risk situation, it is necessary to enhance urban disaster resilience. Based on the disaster resilience process of prevention, absorption, and enhancement, and considering the safety factors such as personnel, facility, environment and management, this paper forms a dual dimension of the urban disaster resilience assessment model covering the key elements of urban disaster response and the core capacity of urban disaster recovery. Furthermore, if taking into account the characteristics of rainstorm and flood disasters, the paper screens the key indicators to build up an assessment index system of an urban rainstorm and flood disaster. The practical application was implemented in Beijing to have an assessment of the ability to recover from rainstorm and flood disasters in all districts of Beijing. And then, some pertinent suggestions for enhancing the resilience of Beijing to rainstorm and flood disasters were proposed. [ABSTRACT FROM AUTHOR]

Accurate air quality prediction is of great significance for pollution prevention and disaster prevention. Effective and reliable prediction models are needed not only for short time prediction, but are even more important for long time-series future predictions. In the long time series, most of the current models might not function as accurately as in the short period and thus a new model is required. In this paper, the new PM2.5 predictor is proposed to achieve accurate long time series PM2.5 prediction in Beijing. The predictor simplifies the input parameters through Spearman correlation analysis and implements the long time series prediction through Informer. The results show that AQI, CO, NO2, and PM10 concentrations are selected from the air quality data, and Dew Point Temperature (DEWP) and wind speed are incorporated from two meteorological data to better improve the prediction efficiency by almost 27%. By comparing with LSTM and attention-LSTM models, the model constructed in this paper performs well in different prediction time periods, with at least 21%, 19%, 28%, and 35% improvement in accuracy in four prediction time series: 48 h, 7 days, 14 days, and 30 days. In conclusion, the proposed model is proved to solve the problem of predicting long time series PM2.5 concentrations in the future, which can make up for the shortcomings of the currently existing models and have good application value. [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]

With the continuous development of public transportation, the impact of unexpected events on the operation of bus networks has become increasingly severe due to the growing demand for public transportation and passenger volume. To accurately assess the impact of unexpected events on the operation of bus networks and scientifically evaluate their resilience, this paper proposes a framework for analyzing the resilience of bus networks. With the aim of providing scientific evidence to enhance the reliability of public transportation networks, this framework can be used to determine the resilience of bus networks to unexpected events. The main contributions of this framework include three aspects: 1. Construction of the CRITIC–entropy weighting model for screening and calculating key indicators of the resilience of the bus network; 2. Use of resilience cycle theory to construct a model for analyzing the resilience of bus routes, and design a set of resilience quantification factors to calculate the resilience of bus routes; 3. Use of complex network theory to construct a model for analyzing the resilience of the bus network, by taking the bus route resilience obtained in the second step as the edge weight to calculate the resilience of the bus network. This paper takes the Beijing public transit system as an example and uses real data to verify the accuracy, scientificity, and feasibility of the proposed framework for analyzing the resilience of public transit networks to sudden events. The resilience analysis framework constructed in this paper has improved the existing research on transportation network resilience in theoretical aspects. Furthermore, the results outputted by this framework can provide a decision-making basis for network adjustment and disaster recovery for the management departments of public transportation networks in practical applications. [ABSTRACT FROM AUTHOR]

With the process of urbanization in China, the urban waterlogging caused by rainstorms occurs frequently and often leads to serious damage to the natural environment, human life, and the city economy. Rapid detection of rainstorm and urban waterlogging disasters is an essential step to minimize these losses. Weibo, a popular microblog service in China, can provide many real-time Weibo posts for rapid detection. In this paper, we propose a method to identify microblogs with rainstorm and waterlogging information and apply them to waterlogging risk assessment. After pre-processing the microblog texts, we evaluate the performance of clustering (k -means) and classification (support vector machine, SVM) algorithms in the classification task. Apart from word vector features, we also introduce sentiment and publisher features for more real-time and accurate results. Furthermore, we build a waterlogging intensity dictionary to assess the waterlogging risk from the Weibo texts and produce a risk map with ArcGIS. To examine the efficacy of this approach, we collect Weibo data from two rainstorms and waterlogging disasters in Beijing city as examples. The results indicate that the SVM algorithm can be applied for a real-time rainstorm and waterlogging information detection. Compared to official-authentication and personal-certification users, the microblogs posted by general users can better indicate the intensity and timing of rainstorms. The location of waterlogging points is consistent with the risk assessment results, which proves our proposed risk assessment method can be used as a reference for timely emergency response. [ABSTRACT FROM AUTHOR]

The safety management of university laboratories has always been the primary and prominent problem that puzzles the safety of university laboratories. In view of the characteristics of imperfect laboratory safety management system and mechanism, weak safety awareness of experimental teachers and students, crowded experimental equipment, irregular management and unreasonable layout in laboratories of science and engineering colleges, which pose significant safety hazards, this paper expounds the characteristics of pertinence, operability and statistical analysis of laboratory safety inspection system for laboratory hazard management in combination with the actual situation of the laboratory of School of Chemistry and Environmental Engineering of a university in Beijing, as well as the problems existing in the use of the system and suggestions for improvement, so that the laboratory safety inspection system can guarantee the laboratory safety and management. [ABSTRACT FROM AUTHOR]

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to analyze the comprehensive risks of natural disasters quantitatively and improve the accuracy of natural disaster management and control, this paper expands the F indicator, Forecast, which is about real-time monitoring and early warning data of natural disasters, and forms the flood control risk trend analysis model framework based on PSR. The framework is named FPSR, i.e. Forecast-Pressure-State-Response, composed of static data and dynamic data. By establishing the four-level index system of flood control risk trend analysis in Fangshan District of Beijing, screening factors, and using analytic hierarchy process, AHP, and experts scoring to determine the weights of each factor, it constructs the flood control risk trend analysis model, FCRTAM. At last, using the real-time monitoring and early warning data of natural disasters in Beijing and the information such as disaster-causing factors, historical natural disasters, major hidden dangers, disaster-bearing bodies, disaster reduction resources (capacities), etc., from National Natural Disaster Comprehensive Risk Census in Fangshan, it analyzes the flood control situation of each town in Fangshan. The results show that the results flood control risk index calculated according to FCRTAM is basically consistent with the actual flood control situation of the towns in Fangshan, and can provide theoretical basis for flood control comprehensive risk trend analysis and the decision-making of disaster prevention and reduction in Fangshan District, which has high use value. [ABSTRACT FROM AUTHOR]

This paper studies a location-allocation problem to determine the selection of emergency shelters, medical centers, and distribution centers after the disaster. The evacuation of refugees and allocation of relief resources are also considered. A mixed-integer nonlinear multiobjective programming model is proposed to characterize the problem. The hierarchical demand of different refugees and the limitations of relief resources are considered in the model. We employ a combination of the simulated annealing (SA) algorithm and the particle swarm optimization (PSO) algorithm method to solve the complex model. To optimize the result of our proposed algorithm, we absorb the group search, crossover, and mutation operator of GA into SA. We conduct a case study in a district of Beijing in China to validate the proposed methodology. Some computational experiments are conducted to analyze the impact of different factors, such as the target weight setting, selection of candidate shelters, and quantity of relief resources. [ABSTRACT FROM AUTHOR]

Pre-warning plays an important role in emergency handling, especially in urban areas with high population density like Beijing. Knowing the information dissemination mechanisms clearly could help us reduce losses and ensure the safety of human beings during emergencies. In this paper, we propose the models of pre-warning information dissemination via five classical media based on actual pre-warning issue processes, including television, radio, short message service (SMS), electronic screens, and online social networks. The population coverage ability and dissemination efficiency at different issue time of these five issue channels are analyzed by simulation methods, and their advantages and disadvantages are compared by radar graphs. Results show that SMS is the most appropriate way to issue long-term pre-warning for its large population coverage, but it is not suitable for issuing urgent warnings to large population because of the limitation of telecom company's issue ability. TV shows the best performance to combine the dissemination speed and range, and the performance of radio and electronic screens are not as satisfactory as the others. In addition, online social networks might become one of the most promising communication method for its potential in further diffusion. These models and results could help us make pre-warning issue plans and provide guidance for future construction of information diffusion systems, thus reducing injuries, deaths, and other losses under different emergencies. [ABSTRACT FROM AUTHOR]

Featured Application: This paper examines rainstorm-related micro-blogging activities in response to rainstorms in an urban environment at fine spatial and temporal scales. Results could be used in supporting disaster assessment and mitigation decision making. Natural disasters cause significant casualties and losses in urban areas every year. Further, the frequency and intensity of natural disasters have increased significantly over the past couple of decades in the context of global climate change. Understanding how urban dwellers learn about and response to a natural hazard is of great significance as more and more people migrate to cities. Social media has become one of the most essential communication platforms in the virtual space for users to share their knowledge, information, and opinions about almost everything in the physical world. Geo-tagged posts published on different social media platforms contain a huge amount of information that can help us to better understand the dynamics of collective geo-tagged human activities. In this study, we investigated the spatiotemporal distribution patterns of the collective geo-tagged human activities in Beijing when it was afflicted by the "6-22" rainstorm. We used a variety of machine learning and statistical methods to examine the correlations between rainstorm-related microblogs and the rainstorm characteristics at a fine spatial and a fine temporal scale across Beijing. We also studied factors that could be used to explain the changes of the rainstorm-related blogging activities. Our results show that the human response to a disaster is very consistent, though with certain time lags, in the virtual and physical spaces at both the grid and city scales. Such a consistency varies significantly across our study area. [ABSTRACT FROM AUTHOR]

As the ancient capital of the Six Dynasties and the current capital, Beijing has a rich historical and cultural heritage. As a carrier of culture, cultural preservation units have significant historical, artistic, and scientific value in their own right. In this paper, we use the mean nearest neighbor, kernel density analysis, and standard deviation ellipse tools in ArcGIS 10.2 software to analyze the spatial and temporal distribution characteristics of different types and periods of cultural heritage units in Beijing and their potential seismic and meteorological hazard risks, to provide strong data support for the conservation and use of cultural heritage units in Beijing. The results show that the different types of cultural preservation units in Beijing are unevenly distributed, with the two categories of modern important historical sites and representative buildings and ancient buildings being more numerous and concentrated in Dongcheng District, Xicheng District, and Haidian District in central Beijing, and the three categories of ancient tombs, ancient ruins, cave temples, and stone carvings being less numerous and concentrated in the outer administrative districts of Beijing. In recent years, the relative humidity in Beijing has shown a decreasing trend, while the average temperature has shown an increasing trend, and this dry and hot environment is not conducive to the conservation of cultural preservation units. This dry and hot environment is not conducive to the protection of cultural preservation units and is prone to damage such as cracking, collapse, deformation, and discoloration of bamboo, wood, rocks, and other cultural heritage elements. It is concluded that in terms of specific regions, the number of cultural heritage units in central Beijing is high, the period is late and the risk of seismic hazards is high, while the number of cultural heritage units in the outer administrative regions of Beijing is low, the period is early and the risk of seismic hazards is low. In conclusion, to enhance the conservation and use of cultural heritage units in Beijing, the relevant authorities should strengthen the daily management of the three high-density and high-risk areas of cultural heritage units in Dongcheng District, Xicheng District, and Haidian District, while at the same time not relaxing the emergency disaster prevention and mitigation of the smaller number and earlier period of cultural heritage units in the peripheral administrative districts. [ABSTRACT FROM AUTHOR]

This paper introduces the computable general equilibrium (CGE) model into the comprehensive economic loss assessment due to rainstorm, using Beijing as an example. The article has constructed a regional storm disaster CGE model to measure the impacts made by rainstorm occurence on sectors and the whole economic system by adding four disaster impact parameters corresponding to the four most affected sectors. The evaluation results show that the torrential rain on July 21 of 2012 in Beijing has caused a total of more than 27 billion RMB of indirect economic loss, which is much more than 11.64 billion RMB of direct economic loss, and on the other hand, we find that, although the rainstorm has brought immense losses to multiple economic departments, it stimulates the increase of social employment rate and investment in fixed assets. [ABSTRACT FROM AUTHOR]

The 18th International Symposium on Geo-disaster Reduction (ISGdR) was held on 20–22 November in Beijing, China, focusing on the theme of "Improving the Relationship between Geoenvironment and Society". In this symposium, a high-level Gu Dezhen Lecture and a number of keynote and invited lectures provided a platform for scientists, industrial professionals and students to share their researches and exchange novel ideas on geo-disaster reduction in a hybrid way of offline and online. [ABSTRACT FROM AUTHOR]

In view of the transactional and textual features on issue handling in mega-event traffic contingency plan, this paper gives a quantitative method for emergency resources location and allocation. Given that the requirement on safeguards in the sports mega-events is temporary and stringent, we first divide the facilities into temporary emergency facilities and fixed emergency facilities and the resources into material resources and human resources. Considering the uncertainty of emergency incidents, we then construct a mixed integer linear programming model. To solve this model, the bisection method is used to import the material quantity placed in each emergency facility, and the shortest path algorithm is used to import the rescue time matrix. Considering the slowness of convergence rate when the road network is large, a modified matrix real-coded genetic algorithm is designed with the crossover operator based on a greedy algorithm. The application of the model and algorithms is validated by the case based on 2022 Beijing Winter Olympics. Sensitivity analysis of some important parameters is also conducted to provide insights for traffic emergency resources management in sports mega-event. [ABSTRACT FROM AUTHOR]

Improving social concerns relating to floods and the perception of the public to risk can reduce the risk of urban flood disasters. This study investigates whether social concern regarding disaster is greatly increased by an extraordinary rainstorm, and whether such disasters change the public's risk perception and stimulate them to take effective disaster prevention measures. An extraordinary rainstorm disaster-known the 7/21 disaster-occurred in Beijing, China, on July 21, 2012. This resulted in 79 deaths and a direct economic loss of 11.64 billion RMB (approximately 1.80 billion USD). Using this disaster as an example, this paper analyzes Beijing's daily precipitation data in the 2012 and 2013 flood seasons and measures the degree of social concern before and after the disaster and the relationship between the degree of social concern and the precipitation intensity. The results indicate that the rainstorm rapidly increased the degree of social concern from 0.12 to 0.77; however, concern had stabilized by 2013 and was basically consistent with the precipitation amounts. A random sample of 181 people participated in a questionnaire survey. The results indicate that the level of public risk perception significantly increased post-disaster, with the degree of public concern regarding rainstorm risk and the number of people who implemented disaster prevention and preparedness measures being doubled. Moreover, approximately 75 % of the disaster prevention behavior involved observing early warning information and going out less, i.e., reducing exposure. But, this response was only a temporary adjustment phenomenon, lasting approximately 1 month: Entry-Transition on July 21 and Exit-Transition on August 30. Moreover, public behavior changes are limited to the year in which a disaster occurs: In the flood season of 2013, 62 % of the population did not appear to be affected by the 2012 precipitation results. Therefore, the majority of the public will not establish long-term risk prevention awareness as a result of an extraordinary disaster. Strengthening public awareness of disaster risk requires a long-term effort. [ABSTRACT FROM AUTHOR]

Regional vulnerability research is helpful in understanding the formation mechanism of regional disaster risk deeply. Owing to the pressure from earthquake disasters occurred frequently in the whole world, the assessment of regional vulnerability to seismic hazards is a focus in current research of seismic disaster risk. This paper presents a nonlinear dynamic model for assessing regional vulnerability to seismic hazards based on maximum flux principle (MFP). Firstly, a vulnerability index system was designed for the urban agglomeration of Beijing, Tianjin and Hebei in China, and the index data were obtained based on the Statistical Yearbook and other materials. The vulnerability indexes constituted a complex system, and the indexes were taken as the components of the complex system. Secondly, based on the information theory and MFP, a dynamic equation that controls the generation and evolution of the complex system was derived theoretically. The regional vulnerability evaluation was realized by conferring reasonable weight to the indexes to make the system become stable. Finally, the self-organizing neural network was used to simulate the mechanism model to implement vulnerability assessment. The assessment results showed that Langfang and Chengde were at high vulnerability level; Qinghuangdao, Cangzhou and Zhangjiakou were at moderate vulnerability level; Baoding, Shijiazhuang, Beijing and Tangshan were at lower vulnerability level; Tianjin was at low vulnerability level. It reveals that the investment in disaster prevention was in an unbalanced situation among all the cities in Beijing-Tianjin-Hebei metropolitan area. [ABSTRACT FROM AUTHOR]

Abstract: With the urban rail transit network in Beijing becoming gradually intensive, the passenger flow organization also develops towards the type of the network mode. This paper analyzes the development trend of the urban rail transit network in Beijing. On the basis of a reasonable classification of emergencies occurred in urban rail transit, this paper puts forward the passenger organization model of rail transit network under emergency conditions, and then set a certain train accident of Beijing Metro Line 1 as an example to illustrate the practical application of the passenger emergency organization mode. [Copyright &y& Elsevier]

Copyright of Research of Environmental Sciences is the property of Research of Environmental Sciences Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Passenger flow distribution in the metro system is fundamental for many applications such as network planning and design, passenger flow forecasting, individual travel activity modeling and emergency response management. However, in most metro systems the smart card automated fare collection (AFC) equipment in Beijing only record when and where a passenger enters and leaves the metro network. Therefore, how to accurately determine passenger flow distribution in unknown travel routes remains a challenging task for the managers. This paper presents a methodology for reconstructing metro passenger flow distribution from large-scale smart card data. A clustering method was first applied to group the travel time of passengers between origin–destination (OD) station pairs into different clusters. Then an approach was proposed that considered both uncertain walking time and transfer time, to estimate the theoretical travel time of all possible routes between the OD pair. An approach to measure the similarity was further employed to match each travel time cluster to a most-likely travel route, and finally obtained the passengers' flow of every route. Compared with two classical methods, the proposed approach was more accurate and efficient. • A data-driven methodology is proposed to obtain metro passenger flow distributions. • A similarity measure model is built to identify possible routes by clustering result. • The travel times are clustered to obtain the possible travel time groups. • Real data from the Beijing metro smart card system is applied to verify our approach. [ABSTRACT FROM AUTHOR]

The waterlogging disaster has become a major threat to the resilient development of cities, which needs a unified and systematic accounting framework to estimate potential waterlogging effects in terms of forming disaster prevention strategies. In this paper, we developed an emergy-based waterlogging footprint accounting framework applying depth-damage functions, input–output (IO) analysis and ecological footprint, considering both economic and environmental factors. Taking the old town Beijing as a case study, we simulated the urban waterlogging distribution with a hydrodynamic model (InfoWorks ICM) over different return periods (1, 2, 3, 5, 10, 20, and 50 years) at high resolution. This study first considered the economic impact caused by urban waterlogging to evaluate the direct economic loss and indirect economic loss by using the depth-damage function and IO model, respectively. The environmental loss was assessed with an ecological footprint and was unified by emergy with economic loss. Then, four emergy-based indicators were established to quantify the specific impacts of waterlogging on urban resilience. The results show that the waterlogging footprints grew constantly by 284.57% from 1 year to 50 years. The waterlogging footprints driven by environmental loss, which were overlooked, accounted for 13.02% of the total. The waterlogging footprint density and waterlogging resilience index showed high spatial heterogeneity, indicating that the sensitivity of subdistricts to waterlogging varies greatly. Our study provides a useful energy-based tool for assessing urban waterlogging losses and supports decision-making for waterlogging disaster mitigation and risk zoning management. • An emergy assessment of hydrological-based urban waterlogging loss was first conducted. • Losses of natural capital due to urban waterlogging disasters were quantified. • Economic and environmental losses were incorporated. • Urban waterlogging resilience was explained in context of metabolism. [ABSTRACT FROM AUTHOR]

Urban resilience assessment is crucial to guarantee the safe and stable operation of our cities. Looking at the whole process of emergencies from the occurrence, development, causing disaster losses to taking emergency response measures, we combine the danger of urban accident and disaster, the capacity of disaster-bearing carriers, and the emergency management capacity as the influencing factors. Taking Beijing as an example, considering the completeness of the relevant indicators and the difficulty of obtaining data, TOPSIS is used to construct a multi-factor comprehensive assessment model of urban safety resilience; At the same time, the important factors constraining the improvement of safety resilience are further analyzed by using the obstacle degree model. The results show that: among the 16 districts in Beijing, Chaoyang and Haidian districts have the highest level of safety resilience; the level of the degree of disaster tolerance among the multi-factors has the greatest impact on the level of urban safety resilience in Beijing; and the multifactorial comprehensive assessment model of urban resilience is able to reflect the urban resilience distribution, discover the weaknesses that exist in the development of urban safety and provide decision-making aids for the relevant city managers to carry out urban resilience construction. [ABSTRACT FROM AUTHOR]

Remote sensing data have been widely used in research on population spatialization. Previous studies have generally divided study areas into several sub-areas with similar features by artificial or clustering algorithms and then developed models for these sub-areas separately using statistical methods. These approaches have drawbacks due to their subjectivity and uncertainty. In this paper, we present a study of population spatialization in Beijing City, China based on multisource remote sensing data and town-level population census data. Six predictive algorithms were compared for estimating population using the spatial variables derived from The National Polar-Orbiting Partnership/ Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) night-time light and other remote sensing data. Random forest achieved the highest accuracy and therefore was employed for population spatialization. Feature selection was performed to determine the optimal variable combinations for population modeling by random forest. Cross-validation results indicated that the developed model achieved a mean absolute error (MAE) of 2129.52 people/km2 and a R2 of 0.63. The gridded population density in Beijing at a spatial resolution of 500 m produced by the random forest model was also adjusted to be consistent with the census population at the town scale. By comparison with Google Earth high-resolution images, the remotely-sensed population was qualitatively validated at the intra-town scale. Validation results indicated that remotely sensed results can effectively depict the spatial distribution of population within town-level districts. This study provides a valuable reference for urban planning, public health and disaster prevention in Beijing, and a reference for population mapping in other cities. [ABSTRACT FROM AUTHOR]

Earthquakes can have a tremendous impact on the operations of university campuses. Since campus buildings are used for teaching, living, and entertaining, it is necessary to establish timely and effective emergency response strategies that accord with the functions of each building. Since there is currently no emergency response system that can provide real-time feedback and enable decision making during an earthquake, this paper proposes a university emergency response system based on multi-source data monitoring. The proposed system offers priorities for rescuing people and maintaining facilities according to five indices: the number of people in the building, the building's command role, the potential danger of hazardous chemicals in the building, the extent of damage to the building, and the lifeline role of facilities in the building. Several methods are used in the process of decision making, including the analytic hierarchy process (AHP), linear regression, earthquake disaster simulation, and expert evaluation. The emergency response system is then applied to a case study of the Shahe Campus of the Central University of Finance and Economics in Beijing, where simulations of three typical scenarios under fixed seismic parameters are carried out and compared. The system proposed in this paper makes the campus's earthquake emergency response more rapid and reasonable, providing new ideas for promoting the development of emergency management systems and sustainable campus operations at colleges and universities. Multi-source data based university emergency response system is proposed. The decision framework is timely and effective under limited cost. Specific buildings' electricity consumption reflects the real-time indoor population. Time characteristics and key areas based emergency resources should be allocated. [ABSTRACT FROM AUTHOR]

Population distribution data with high spatiotemporal resolution are of significant value and fundamental to many application areas, such as public health, urban planning, environmental change, and disaster management. However, such data are still not widely available due to the limited knowledge of complex human activity patterns. The emergence of location-based service big data provides additional opportunities to solve this problem. In this study, we integrated ambient population data, nighttime light data, and building volume data; innovatively proposed a spatial downscaling framework for Baidu heat map data during work time and sleep time; and mapped the population distribution with high spatiotemporal resolution (i.e., hourly, 100 m) in Beijing. Finally, we validated the generated population distribution maps with high spatiotemporal resolution using the highest-quality validation data (i.e., mobile signaling data). The relevant results indicate that our proposed spatial downscaling framework for both work time and sleep time has high accuracy, that the distribution of the population in Beijing on a regular weekday shows "centripetal centralization at daytime, centrifugal dispersion at night" spatiotemporal variation characteristics, that the interaction between the purpose of residents' activities and the spatial functional differences leads to the spatiotemporal evolution of the population distribution, and that China's "surgical control and dynamic zero COVID-19" epidemic policy was strongly implemented. In addition, our proposed spatial downscaling framework can be transferred to other regions, which is of value for governmental emergency measures and for studies about human risks to environmental issues. [ABSTRACT FROM AUTHOR]

The Xishan coal mine area in Beijing, China has a long history of mining. Many landslide hazards, in addition to collapses and ground fractures, have occurred in this area. This study used multi-temporal satellite images to extract this region's deformation information, identify landslides and analyze the deformation evolution process of these landslides. Taking the Anzigou ditch as an example, we investigate the "Quarry–Landslide–Mudslide" disaster chain model. We found that the landslide evolution process is closely related to the geological conditions, and usually goes through four stages: initial deformation, slope front swelling and collapsing, rear part connecting and rupturing, and landslide creeping. The surface deformation can be identified and tracked by high-resolution optical images and InSAR monitoring. Under the combined effects of rainfall and topographic conditions, medium and large landslides may occur and trigger a "Quarry–Landslide–Mudflow" disaster chain. The identification and analysis of these landslide hazards and the disaster chain help with geological disaster prevention, and provide reference for early identification and research of similar disasters. [ABSTRACT FROM AUTHOR]

Accurate mapping of dams can provide useful information about geographical locations and boundaries and can help improve public dam datasets. However, when applied to disaster emergency management, it is often difficult to completely determine the distribution of dams due to the incompleteness of the available data. Thus, we propose an automatic and intelligent extraction method that combines location with post-segmentation for dam detection. First, we constructed a dataset named RSDams and proposed an object detection model, YOLOv5s-ViT-BiFPN (You Only Look Once version 5s-Vision Transformer-Bi-Directional Feature Pyramid Network), with a training method using deep transfer learning to generate graphical locations for dams. After retraining the model on the RSDams dataset, its precision for dam detection reached 88.2% and showed a 3.4% improvement over learning from scratch. Second, based on the graphical locations, we utilized an improved Morphological Building Index (MBI) algorithm for dam segmentation to derive dam masks. The average overall accuracy and Kappa coefficient of the model applied to 100 images reached 97.4% and 0.7, respectively. Finally, we applied the dam extraction method to two study areas, namely, Yangbi County of Yunnan Province and Changping District of Beijing in China, and the recall rates reached 69.2% and 81.5%, respectively. The results show that our method has high accuracy and good potential to serve as an automatic and intelligent method for the establishment of a public dam dataset on a regional or national scale. [ABSTRACT FROM AUTHOR]

Knowing the dynamic road risk for pedestrian evacuation and having an efficient evacuation plan play a very important role in the serious disasters such as earthquake, tsunami and hurricane. In this paper, the dynamic road risk for pedestrian evacuation in a densely populated area of Beijing was studied with consideration of different influencing factors. Firstly, the eight influencing factors including road width, node degree, safety betweenness, road resistor coefficient, building threat, pedestrian counterflow, illegal vehicle parking and traffic flow were considered to assess the road risk for pedestrian evacuation. Secondly, based on complex network theory, electric circuit theory and real situation of the roads, the comprehensive assessment function for road risk was developed quantitatively based on the eight influencing factors. Thirdly, we analyzed road risk for pedestrian evacuation considering different situations: current condition, regular condition, and optimal condition; the risk distribution maps were drawn to directly show the risk level. Through assessments, the roads with high risk for pedestrian evacuation were found, and an optimized evacuation plan was obtained and analyzed. This mathematical model can guide the emergency evacuation in real time. The process and the results are essential for improving the efficiency of evacuations which should considerably reduce the possibility of injuries, deaths and other losses in the disaster. [ABSTRACT FROM AUTHOR]

When disaster relief is executed by a monopolistic emergency management agency, this entity is responsible for the procurement and storage of relief goods in the given area. In the case of extreme disasters, relief goods are generally donated to the affected area from all over the world, but the delivery of these donations normally takes time. To ensure effective disaster relief, the emergency management agency should consider extreme disaster scenarios in the procurement of relief goods. Besides, both the supply and demand of relief goods can be differently affected by the disaster depending on its magnitude. And the management agency aims to minimize costs while accounting for potential shortages. In this study, we propose and develop a combined entrusted reserve and option (CEO) contract for humanitarian relief goods procurement. In our model, both the supply and demand of relief goods are described as functions of disasters intensity. Further, a bi-objective optimization problem is formulated with the model to minimize the management agency's expected cost and conditional value-at-risk (CVaR) based shortage risk. Then, we use the epsilon-constraint method to solve the problem and obtain the optimal CEO contract. We also conduct a case study of disasters occurred in Beijing, China from 2000 to 2014. The CEO contract is shown to alleviate losses related to the disasters and reduce costs of the management agency. Insights to further improve the effectiveness of the CEO contract are also discussed in the paper. • A supply risk model which is dependent on disaster intensity is constructed. • A demand model which is dependent on disaster intensity is constructed. • The shortage risk of the extreme disaster is considered. • A CEO contract is proposed for the relief goods procurement. • Sensitivity analyses based on case study provide practical managerial insights. [ABSTRACT FROM AUTHOR]

Quickly and accurately extracting buildings from remote sensing images is essential for urban planning, change detection, and disaster management applications. In particular, extracting buildings that cannot be sheltered in emergency shelters can help establish and improve a city's overall disaster prevention system. However, small building extraction often involves problems, such as integrity, missed and false detection, and blurred boundaries. In this study, EfficientUNet+, an improved building extraction method from remote sensing images based on the UNet model, is proposed. This method uses EfficientNet-b0 as the encoder and embeds the spatial and channel squeeze and excitation (scSE) in the decoder to realize forward correction of features and improve the accuracy and speed of model extraction. Next, for the problem of blurred boundaries, we propose a joint loss function of building boundary-weighted cross-entropy and Dice loss to enforce constraints on building boundaries. Finally, model pretraining is performed using the WHU aerial building dataset with a large amount of data. The transfer learning method is used to complete the high-precision extraction of buildings with few training samples in specific scenarios. We created a Google building image dataset of emergency shelters within the Fifth Ring Road of Beijing and conducted experiments to verify the effectiveness of the method in this study. The proposed method is compared with the state-of-the-art methods, namely, DeepLabv3+, PSPNet, ResUNet, and HRNet. The results show that the EfficientUNet+ method is superior in terms of Precision, Recall, F1-Score, and mean intersection over union (mIoU). The accuracy of the EfficientUNet+ method for each index is the highest, reaching 93.01%, 89.17%, 91.05%, and 90.97%, respectively. This indicates that the method proposed in this study can effectively extract buildings in emergency shelters and has an important reference value for guiding urban emergency evacuation. [ABSTRACT FROM AUTHOR]

Abstract: Keep in pace with the 2008 Beijing Olympics and the 2010 Shanghai Expo, engineering events of different types and sizes are coming to China more than it had ever happened during years. Obviously, these could all cause safety problems. To organize the event perfectly, a lot of questions occur: How to provide the best safety to everyone participate in our event? How to deal with the emergencies during the event? These questions are the most important element of holding a successful event. This paper discusses the necessity of engineering risk management during events. [Copyright &y& Elsevier]

Abstract: To take into account reasonably the safety requirements of the disabled on emergency evacuation in the design, usage, management and emergency response of public places, the authors of this paper organized on-site experiments for 100 disabled individuals (40 people with no supporting tools, 20 people using a single crutch and 40 people two crutches) and 17 healthy people at a subway station in Beijing. The experiments were designed to measure the unimpeded free walking speed of the participants during horizontal and stair ascending/descending movement, and to assess the possible impact of passage width on movement speed. Based on the results of the tests, the authors systematically analyzed the speed variation patterns with disability grade, gender and passage width. The validity of the value of an important parameter for evacuation safety design in the prescribed fire codes in mainland China, the width of a single stream of people, is assessed. Finally the results of the experiments are compared with that from other researches to identify the similarities and differences between them. [Copyright &y& Elsevier]

When responding to crises, a joint approach is often used, which requires coordination among government agencies and other institutions. In this article we combine the vertical and horizontal dimensions to develop a theoretical framework to explain the patterns and performance of coordination. By drawing on structural-instrumental and institutional perspectives, we examine the coordination structures and mechanisms in China's crisis management at the central and local levels. We outline the main structural characteristics of the Chinese system and the overall challenges it faces. We then discuss four crises that have happened in and around Beijing, using these to illustrate the crucial factors driving coordination successes and failures. Lastly, we discuss some comparative features and the theoretical and policy implications of our main results. [ABSTRACT FROM AUTHOR]

Extreme precipitation caused by global climate change is expected to have a severe impact on urbanized areas. While decision‐makers struggle with climate uncertainty, an effective infrastructure adaptation strategy attaches great importance to preventing disasters resulting from rainfall. We propose a decision‐making model to incorporate the probability of rainfall disasters and recommend investing time when evaluating projects related to climate adaptation. We use a hydrological statistical model and economic and technical factors to estimate the expected economic losses in several rainfall disaster scenarios, and the value of the adaptation infrastructures is calculated using a real options pricing approach. Then the decision‐making model is applied to a case study involving a campus rainfall disaster prevention facility at the Central University of Finance and Economics in Beijing, China. We established three submerged scenarios with different rainfall intensities, then we evaluated the premium of holding an option to defer and pointed out the optimal investing time in each scenario. This model is expected to provide guidance for the development of adaptation infrastructure for relatively small areas such as communities and universities. And we proved that using real options‐based approach could provide more managerial flexibility for investors. [ABSTRACT FROM AUTHOR]

Vulnerability to hazards includes not only components of a physical nature, but also those arising from social factors. In developing measures for disaster prevention or emergency response for disaster relief of big cities, an analysis of social vulnerability is very necessary but quite difficult. In order to address the problem, using Beijing as an example, we established a social vulnerability index system including 26 factors and developed an improved analytic hierarchy process (AHP) method. The weight of each factor was evaluated using the improved AHP process that obviously increased the passing rate of consistency of the experts' questionnaire. The population, career, economy, infrastructure and social vulnerability distribution maps of Beijing were obtained. From them, it's easy to see the characteristics of various vulnerability distributions. Through sensitivity analysis, the influencing factors of each area were listed in order of importance. The approach are useful for assessing, reducing the social vulnerability of big cities in China. [ABSTRACT FROM AUTHOR]

Social media has been applied to all natural disaster risk-reduction phases, including pre-warning, response, and recovery. However, using it to accurately acquire and reveal public sentiment during a disaster still presents a significant challenge. To explore public sentiment in depth during a disaster, this study analyzed Sina-Weibo (Weibo) texts in terms of space, time, and content related to the 2018 Shouguang flood, which caused casualties and economic losses, arousing widespread public concern in China. The temporal changes within six-hour intervals and spatial distribution on sub-district and city levels of flood-related Weibo were analyzed. Based on the Latent Dirichlet Allocation (LDA) model and the Random Forest (RF) algorithm, a topic extraction and classification model was built to hierarchically identify six flood-relevant topics and nine types of public sentiment responses in Weibo texts. The majority of Weibo texts about the Shouguang flood were related to "public sentiment", among which "questioning the government and media" was the most commonly expressed. The Weibo text numbers varied over time for different topics and sentiments that corresponded to the different developmental stages of the flood. On a sub-district level, the spatial distribution of flood-relevant Weibo was mainly concentrated in high population areas in the south-central and eastern parts of Shouguang, near the river and the downtown area. At the city level, the Weibo texts were mainly distributed in Beijing and cities in the Shandong Province, centering in Weifang City. The results indicated that the classification model developed in this study was accurate and viable for analyzing social media texts during a disaster. The findings can be used to help researchers, public servants, and officials to better understand public sentiments towards disaster events, to accelerate disaster responses, and to support post-disaster management. [ABSTRACT FROM AUTHOR]