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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]

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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]

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]

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]

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]

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]