Disaster Conditions Research Articles

Демографічні втрати балканських країн унаслідок воєн

Looking at the current state in which Ukraine found itself due to the Russian-Ukrainian war, many questions arise regarding the possibility of estimating demographic losses resulting from this war. A study of the demographic losses of the Western Balkan countries during the most recent wars in this region is a useful preparation for approbation of a methodological basis for future calculations. Therefore, it is currently relevant to study the specifics of the use of general methodological approaches for determining losses under the conditions of a specific social disaster; to assess the demographic losses of the republics of the former Yugoslavia resulting from the Balkan wars, taking into account the specifics of the course of the crisis and the available information base; to determine the possibility of applying the used approaches to the assessment of demographic losses resulting from the Russian-Ukrainian war. The purpose of the article is an attempt to estimate the demographic losses of the republics of the former Yugoslavia resulting from the Balkan wars. The tasks are the analysis of available data and research on demographic losses and obtaining authors’ own assessment of them. The scientific novelty is the generalized information from various sources and the study of the specifics of its formation, the authors’ assessment of the excess mortality and the birth deficit in some republics of the former Yugoslavia. For the purposes of the analysis, the authors provided a chronology of events in the Western Balkans of the late 20th and early 21st centuries and identified sources of information on losses for each former Yugoslav republic. However, despite the fact that many sources can be found in open access, they differ in the diversity of data, calculation methodology, and completeness of presentation. The article presents generalized information on the number of dead as a result of military conflicts based on several analytical sites. The authors defined two approaches, according to which demographic losses can be calculated: the first is the assessment of direct and indirect losses; the second is the use of the demographic balance equation, when excess mortality and birth deficit are estimated. In all the resources cited in the article, the determination of losses is carried out according to the first approach, the authors of the article made an attempt to estimate demographic losses according to the second approach. Using this approach, based on Eurostat data, it was possible to determine losses for only three countries: Serbia, Slovenia, and Croatia. For two regions, Eurostat does not provide demographic data for the years of active military operations: for Bosnia and Herzegovina for 1992—1995, for Kosovo for 1998—2001. In Macedonia, the number of casualties was small, which makes it impractical to build a hypothetical model, while Montenegro managed to avoid war altogether. So, both approaches have one common problem — the lack of a reliable information base. However, differences in loss estimates between the two approaches are natural due to the use of different methodological tools. Nevertheless, the magnitude and causes of large differences require additional research. It should be noted that there are quite a lot of estimates of direct losses, but at the time of the study, the authors were not aware of attempts to estimate excess mortality in individual republics of the former Yugoslavia. The obtained estimates require critical analysis and, if necessary, adjustments. The direction is the reconstruction (or clarification) of demographic dynamics (especially for Bosnia and Herzegovina) taking into account long-term demographic trends.

Assessment of Urban Resilience to Floods: A Spatial Planning Framework for Cities

Urbanization-led economic growth drives infrastructure investments and population accumulation in cities, hence exploiting natural resources at an extreme rate. In this context, coastal cities have become vulnerable to climate change-induced extreme weather events and human-made disasters in recent history, where effective measures to improve the resilience of cities are pivotal for developing sustainable living environments. This study proposes a framework for assessing urban resilience to natural disasters (floods) using bottom-up spatial interactions among natural, physical, and social systems within cities and regions. It is noted that seminal studies focus on either the mitigation or adaptation strategies within urban environments to assess disaster resilience, where limited multidisciplinary and operational models hinder evaluations at the city scale. Therefore, urban system interactions and quantifiable parameters proposed in this framework are essential for policymakers and disaster management agencies in the timely allocation of resources to optimize the recovery process. Moreover, spatial planning agencies can adopt resilience mapping to identify the potential risk zones and orient sustainable land use management. Urban resilience can be embodied in spatial strategies with the operational framework proposed here, and future urban growth scenarios can be tested in multiple disaster conditions.

BEW-YOLOv8: A deep learning model for multi-scene and multi-scale flood depth estimation

As global climate change intensifies, the frequency of extreme weather events, including urban flooding, has risen, posing a significant threat to the safety of urban areas. Efficient and precise flood depth monitoring is crucial for a dynamic understanding of disaster conditions, enabling informed decision-making and reducing the potential for significant loss of life and property. Although object detection models have shown promise in estimating flood depths and have achieved notable results, enhancements are still needed in applications across multi-scene and multi-scale. This research enhances the YOLOv8 model by integrating Bidirectional Feature Pyramid Network (BiFPN), Effective Squeeze and Excitation (EffectiveSE), and Wise-IoU (WIoU), targeting submerged cars to develop the BEW-YOLOv8 model. Testing shows that the BEW-YOLOv8 model improves Precision, Recall, F1 Score, mAP@0.5, and mAP@0.5–0.95 by 14.1%, 2.2%, 7.9%, 8.7%, and 6.3%, respectively, compared to the unmodified YOLOv8 model. Compared with other existing models, BEW-YOLOv8 achieves the best performance with less than one-tenth of the parameters. Furthermore, this study verifies that the BEW-YOLOv8 model holds considerable promise for further enhancements with dataset expansions. Its precision and dependability make the BEW-YOLOv8 model well-suited for real-time flood depth assessment across multiple scenes and scales. The model’s capability for real-time processing supports urgent flood response needs, offering solid technical assistance for managing urban floods and mitigating disaster risks.

Penyusunan rencana kontinjensi epidemi penyakit menular disebabkan oleh virus di Kota Banda Aceh [Development of contingency plan for infectious disease epidemics caused by viruses in Banda Aceh City

Contingency Plan for Outbreak/Epidemic Disaster in Banda Aceh City is a document that contains the process of identifying and preparing future plans based on potential disaster conditions. The purpose of making a Contingency Plan document that becomes a reference in the disaster outbreak/epidemic of Infectious Diseases Caused by Viruses in the City of Banda Aceh. Method The preparation process involved all disaster management institutions and actors, both government and non-government. The scope of activities carried out are: Technical Team Meeting, Public Seminar, and Plan Finalization. The results obtained by the issuance of the Banda Aceh Mayor's Regulation for the Contingency Plan for the epidemic of infectious diseases caused by viruses in the city of Banda Aceh which is regulated in a Banda Aceh Mayor's Regulation No. 68 of 2021 concerning the Contingency Plan for the Epidemic of Infectious Diseases Caused by Viruses in the City of Banda Aceh. In conclusion, Banda Aceh City has realized preparedness for the threat of epidemics of diseases caused by viruses by involving multi-sectors. To improve the function and sustainability of the document, data updates and projections of resource needs are needed to support the implementation/activation of the contingency plan that has been prepared.

Investigating of Resilience Scale of Selected Hospitals in Yazd City during Disaster Conditions in 2023

Background: The need for healthcare is one of the fundamental needs of humans in critical and emergency situations. Hospitals and healthcare centers are among the first units that can reduce casualties by providing optimal and timely healthcare services. Therefore, the present study aims to evaluate the resilience status of selected hospitals in Yazd city during the crisis in the year 2023. Methods: This research is a descriptive-cross sectional study conducted in 2023 in 5 selected hospitals in Yazd (classified by hospital ownership: university hospitals A, B, C, charity hospital D, and private hospital E). The resilience level of the hospitals during a crisis was assessed using Zhong's standard questionnaire, which evaluated 9 dimensions of resilience. Responses to the questions were based on a 5-point Likert scale (always to never), and data were analyzed using SPSS 26 software. Quantitative data were reported with central (mean) and dispersion (standard deviation) indicators, followed by qualitative (demographic) data as numbers and percentages. Results: The results showed that the overall resilience status of the hospitals in this study averaged 3.95 ± 0.69, which was evaluated at a good level. The highest score was for the private hospital E with an average of 4.30 ± 0.55, and the lowest score was for the public hospital A with an average of 3.75 ± 0.55. Hospital A's score was at a moderate level, while the remaining hospitals were rated at a good level. Conclusion: Considering the average resilience of hospitals in a number of areas, it is suggested to develop and present training programs related to preparing for critical situations and holding maneuvers and conducting exercises with emphasis on training key employees.

The Influencing Mechanism of Household Food Purchasing Behavior and Household Reserve Efficiency under Non-Normal Conditions

Family reserves are an important part of national reserves, and how to do a good job in family reserves is a common concern of the government and society. Under the non-normal conditions of major accidents and disasters, wars, plagues, social unrest, etc., urban food supply mainly depends on external supply guarantee, and urban residents’ risk perception is more sensitive. Based on the Theory of Planned Behavior and Norm Activation Model, this study constructs an analytical framework for the risk perception, perceived behavior control, and family reserve efficacy of urban residents under non-normal conditions from the perspectives of rationality and sensibility, self-interest, and altruism. The perceived behavior control of household food reserves in non-normal conditions is affected by risk perception, subjective norms, and personal norms. On this basis, countermeasures and suggestions are put forward: Urban residents should strengthen their sense of risk and responsibility for storing food at home, reserve food appropriately, and develop a good habit of family saving. On the other hand, it is necessary to pay attention to personal norms, reduce the negative impact of subjective norms on residents, and avoid excessive food storage and food waste.

WARGA BANTU WARGA: MODAL SOSIAL SEBAGAI INSTRUMEN NON GOVERMENT YANG MEMPERKUAT PEMERINTAHAN DESA DI SIGI KELUAR DARI KRISIS

This article will examine how social capital in rural areas, manifested through the citizen-help-citizen movement, has contributed to residents and village governments overcoming the crisis problems caused by the pandemic. There are two movements of citizens helping residents: the women's network at the neighborhood level and the roa guard roa movement. This research took place in rural Sigi, Central Sulawesi. Data collection was carried out through interviews using fieldwork. We frame these findings through the lens of Non-Governmental Innovation with a starting point in the social capital of citizens. We argue that non-governmental innovation has created natural disaster management. The experience of residents going from one disaster condition to another produces collective navigation for residents. They finally have some knowledge tools that can be used when facing a crisis, such as knowing what to do and how to do it. This knowledge tool helps governments at the village level in minimizing the risk of crisis due to disasters.

ANALYSIS OF MICRO GAS TURBINE PERFORMANCE USING MODELLING APPROACH

Micro-gas turbine (MGT) is compact and self-sufficient distributed power generation units that exhibit reliability and has the capability to mitigate carbon monoxide (CO) emissions while conserving energy together with biomass used as a fuel sources. It is expected that they will have a noteworthy impact on securing the provision of upcoming energy resources for isolated areas, disaster condition and off-grid zone regardless of their connection to the power grid. However, the MGT performance using the various fuel still questionable. Databases and procedure of the MGT performance analysis should be established well to understand the overall MGT performance. In this study, MGT performance is determined by utilising the computer aided design (CAD) and solver software such as SOLIDWORKS and ANSYS-FLUENT. CAD drawing is used to design the combustion chamber and simulation performance is visualised in specific inside the combustion chamber (CC) of the MGT. The solver software was employed for the purpose of conducting computational fluid dynamics (CFD) analyses. The optimal configuration contained a flame holder with a diameter of 50 millimetres, a chamber height of 60 centimetres, four holes with diameters of 6, 8, and 10 millimetres, and a dead zone separating the combustion and dilution zones. The air inlet boundary conditions were established by utilising the specifications and compressor maps of the Garrett GT25 turbocharger in the simulation. The simulation employed the standard k-ε model of viscous model and energy equation to establish an optimal maximum airflow rate of 0.15 kg/s at a pressure of 1.4 bar, resulting in a compressor efficiency of approximately 70%. Three different fuels, diesel-air, wood-volatiles-air, and coal-hv-volatile, were used to determine the MGT's optimal performance. Successful investigation of this study opts to significant contribution to understand the MGT performance thus, give the awareness to choose as one of the alternative power generations which can be used in the future.

Volkmann's ischaemic contracture of the upper extremity - raising a red flag in the setting of developing countries.

Volkmann's ischaemic contracture (VIC) is a disabling condition resulting from tissue necrosis due to impaired vascular supply to the limb. Over the years VIC has become rare in developed countries with many different aetiologies described. It was alarming to have high incidence of established VIC in our practice in Nepal. A detailed analysis was conducted to accurately describe this issue. We collected 47 cases of VIC over six years and noted the age, sex, district of origin and cause of VIC, duration of injury to presentation, and the grade of VIC. Then we compared these characteristics of VIC of each Nepal province and created a map to show the problematic regions. Out of 47 patients, 46 could have been prevented by an early treatment. The most common cause was a tight cast in 25 patients (53.19%), followed by unintentionally self-caused VIC by applying tight bandages in 21 patients (44.68%). Most cases came from province 6 (29.78%). Our group included three mild (6.4%), 35 moderate (74.5%) and nine severe (19.1%) cases of VIC. Only 14 cases (29.78%) had a timely fasciotomy in the past. VIC is an irreversible complication of the compartment syndrome which is an easily preventable condition in the setting of developing countries. Our focus should, therefore, aim at preventing such disastrous conditions as 97.87% of cases we encountered could have been avoided by proper primary care. In the case of Nepal most cases came from province 6 and province 3.

Assessing vessel transportation delays affected by tropical cyclones using AIS data and a bayesian network: A case study of veronica in northwestern Australia

Tropical cyclones frequently disrupt maritime transportation systems, impacting the normal operation of vessels and causing transportation delays. Analyzing the occurrence and degree of vessel transportation delays under disaster conditions is one of the critical aspects of maritime transportation management. This article, based on Automatic Identification System (AIS) data, examined the real behavioral trajectories of vessels and delay characteristics during tropical cyclones. Taking the example of Tropical Cyclone Veronica, which occurred in the waters off northwestern Australia in 2019, we conducted a comprehensive analysis. This involved numerical simulations of the entire disaster process and the cleaning and matching of trajectory data for all affected vessels in the area. We delved deeply into the relationship between vessel behavior characteristics and tropical cyclones, identified factors contributing to delays, and, based on our findings, constructed a Bayesian network inference model for vessel transportation delays under disaster conditions. This model uses information such as tropical cyclone intensity, vessel basic attributes, behavior choices, and port disaster avoidance measures as its primary nodes. The research results indicate that vessel rerouting, vessel loss of control, and waiting for port entry are the three major sources of vessel delays during disasters. Key influencing factors contributing to these consequences include port closure measures, the duration of vessels being adversely affected by strong winds, vessel tendencies toward loss of control, and risk preferences. To control the extent of vessel delays more effectively, it is crucial to prioritize these sensitive factors and strengthen monitoring and management efforts. This model is driven by real data, providing an objective reflection of real-world scenarios, and its effectiveness has been validated through sensitivity analysis. The research perspective and algorithmic framework presented in this paper offer a novel paradigm and fresh perspective for the study of maritime transportation disasters. The research findings have significant implications for bolstering the resilience of maritime transportation networks, enhancing our comprehension and anticipation of delays, and ensuring the continuity, security, and efficiency of the global supply chain for goods.

Environmental impact assessment with rapid impact assessment matrix method: during disaster conditions

In the last several decades, Iran’s ecosystem has suffered due to the careless usage of natural resources. Cities have grown in an uneven and non-normative way, and poor project management has been a major issue, particularly in large cities. An even greater number of environmental factors and engineering regulations are not relevant to projects. Because of this, in order to ascertain a project’s environmental impact, an environmental impact assessment (EIA), is required. Using the rapid impact assessment matrix (RIAM) is one method of applying it to EIA. Reducing subjectivity brings objectivity and transparency. During the COVID-19 pandemic, a thorough EIA was carried out for the Tehran project utilizing the RIAM and other possibilities. This research is the first to combine the methodology that was discussed during the incident. Through the use of the RIAM technique, the environmental impact of COVID-19 was to be quantified in this inquiry. The research examined lockdown procedures and the COVID-19 pandemic to create an EIA indicator. In a real-world case study conducted in Tehran, Iran, the impact of the initiative was evaluated using the RIAM methodology during the COVID-19 epidemic. The results demonstrated that COVID-19 had both beneficial and harmful effects. Decision-makers were effectively informed about the COVID-19 pandemic’s environmental consequences on people and the environment, as well as how to minimize negative effects, according to the EIA technique that used RIAM. This is the first research to integrate the EIA during a crisis, such as the COVID-19 pandemic, with the RIAM approach.

Risk Assessment and Analysis of New Power System Operation Under Extreme Disasters

Abstract In recent years, new power systems have been developing continuously, and the operational risk assessment of new power systems under extreme disaster conditions has attracted much attention. This paper uses Latin hypercube sampling technology to establish a power system failure model under extreme disasters and proposes a new power system risk assessment method that applies the power system power flow model to establish a comprehensive system risk index. In addition, taking the power grid of a certain province in eastern China as an example, a case analysis is conducted on the IEEE39 node system to verify the effectiveness of the method.

Dapur Makanan Pendamping-Air Susu Ibu (MP-ASI) di Daerah Bencana Malampah, Pasaman Barat

Indonesia legislation number 24 of 2007 at 55 concerning disaster management states that children (babies and toddlers) are a vulnerable group when a disaster occurs.1 Children are a vulnerable population and have special health problems that must be considered when a disaster occurs. One of them is the very limited availability of complementary foods (MP-ASI) and this condition will worsen the nutritional status of children in disaster areas.2,3,4 The provision of food menus for toddlers in natural disaster conditions is limited because public kitchen do not prepare special menu for toddlers so that children consume food that is also consumed by adults, including instant food. The availability of MP-ASI ingredient can be achieved from public kitchens with the help and cooperation of various parties including volunteers by setting up MP-ASI kitchen so that nutritional intake for toddlers is fulfilled in disaster conditions.5 This was a activities report on establishing MP-ASI in the disaster area, Malampah, Pasaman. It has been expected to be a reference for establishing MP-ASI kitchen in disaster area, as consideration in creating operational standards for establishing MP-ASI kitchen in disaster area, and assist in managing public MP-ASI kitchen in natural disaster area.

Protocol for the Support Application for Food PAntrieS trial: design, implementation, and evaluation plan for a digital application to promote healthy food access and support food pantry operations.

Food-insecure households commonly rely on food pantries to supplement their nutritional needs, a challenge that was underscored during the COVID-19 pandemic. Food pantries, and the food banks that supply them, face common challenges in meeting variable client volume and dietary needs under normal and emergency (e.g., pandemic, natural disaster) conditions. A scalable digital strategy that has the capacity to streamline the emergency food distribution system, while promoting healthy food options, managing volunteer recruitment and training, and connecting to emergency management systems in times of need, is urgently required. To address this gap, we are developing a working mobile application (app) called the Support Application for Food PAntrieS (SAFPAS) and will evaluate its feasibility and impact on food pantry staff preparedness, stocking, and client uptake of healthful foods and beverages in two urban United States settings. This paper describes the protocol for a randomized controlled trial of the SAFPAS mobile application. We will conduct formative research in Baltimore, Maryland and Detroit, Michigan to develop and refine the SAFPAS app and increase scalability potential to other urban settings. Then we will test the app in 20 food pantries in Baltimore randomized to intervention or comparison. The impact of the app will be evaluated at several levels of the emergency food system, including food pantry clients (n = 360), food pantry staff and volunteers (n = 100), food pantry stock, and city agencies such as the local food bank and Office of Emergency Management. The primary outcome of the SAFPAS trial is to improve the healthfulness of the foods received by food pantry clients, measured using the Food Assessment Scoring Tool (FAST). Post-trial, we will conduct additional formative research in Detroit to prepare the app for scale-up. We anticipate that SAFPAS will improve alignment in the supply and demand for healthy foods among food pantry clients, food pantries, and city agencies which supply food in Baltimore. Real-time, bidirectional communication between entities across the system allows for increased situational awareness at all levels during normal and emergency operations. By conducting formative research in Detroit, we hope to increase the scalability of the SAFPAS app to additional settings nationwide. NCT87654321. https://classic.clinicaltrials.gov/ct2/show/NCT05880004.

A multi-period optimal distribution model of emergency resources for responding to COVID-19 under uncertain conditions

Ideally, optimal emergency resource allocation would have been vital for effective relief work during the COVID-19 outbreak. However, the suddenness of the epidemic and uncertainty of its spread added some difficulties to distributing emergency resources. First, this study introduces triangular fuzzy numbers to describe the uncertainty of supply and demand of emergency resources, and interval numbers to describe the time required for resource transportation under disaster conditions. To minimize the total delivery time and difference in the total satisfaction rate, this study constructs an optimal model for emergency resource distribution under uncertain conditions that considers both efficiency and equity. Subsequently, an improved genetic algorithm (IMGA) is proposed to obtain the optimal decision scheme. Finally, a case study on emergency resource distribution during the COVID-19 pandemic is conducted for model verification. The results demonstrate that the proposed model can improve the efficiency and effect of emergency resource distribution. The model allocates some emergency resources to each demand site during each emergency period, which can help avoid large losses caused by extreme shortages of resources at a certain demand point. The emergency resource allocation scheme considers the transportation time and degree of impact, which is beneficial for enhancing the flexibility of decision-making and practical applicability of distribution operations. A comparative analysis of the algorithms shows that the proposed IMGA is an effective method for managing emergency resource distribution optimization problems because it has higher solving efficiency, better convergence, and stronger stability. These findings can provide decision support for the optimal distribution of large-scale, multiperiod emergency resources during the COVID-19 pandemic.

Developing an integrated blood supply chain network in disaster conditions considering multi-purpose capabilities

PurposeThis study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.Design/methodology/approachThis paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.FindingsThe findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.Originality/valueThe researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

Research on the state of areas in Ukraine affected by military actions based on remote sensing data and deep learning architectures

The invasion of Ukraine by the Russian Federation and the escalation of military actions in the regions have led to significant damage to residential buildings, civilian infrastructure, various critical infrastructure objects, dams, and extensive pollution of the territories. In this context, the tasks of remote sensing using satellite imagery and aerial observation arise to analyze the impact and conduct an economic assessment of damage in these areas. This work investigates and employs deep neural network (DNNs) models in computer vision (CV) tasks (classification, segmentation) and combines their derivatives, such as convolutional networks (CNNs) and vision transformer models (ViTs), to enhance the accuracy of damage assessment. ViTs have demonstrated significant success, often surpassing traditional CNNs, and have potential applications in remote sensing for damage assessment and the protection of critical infrastructure. The research conducted in this work confirms the importance of applying such technologies in environments where labeled data are rare or non-existent, particularly evaluating the use of DNNs, including CNNs and ViTs, in analyzing regions affected by military actions using synthetic aperture radar (SAR) and multispectral images. The aim and subject of this research also include reviewing the possibilities of combining CNNs and ViTs to improve the speed of image feature extraction, landscape detection, and the detection of complex structural contours of objects, where data are usually insufficient. The results of this study provide a critical review of the application of CNNs and ViTs in remote sensing, identifying significant gaps and challenges, especially in the context of the economic consequences of destruction due to military actions. The technical aspects of using CNNs and transformer-based models for complex CV tasks and transfer learning under data-scarce conditions, as well as the challenges in analyzing large volumes of geophysical data, are considered. The conclusions emphasize the transformational potential of DNNs, especially transformers, in remote sensing under conflict and disaster conditions. Their adaptability and accuracy in various environments underscore their utility in both strategic military and humanitarian contexts, establishing a practical standard for their application in key real, real-world scenario-based territory condition assessment.

Flood risk identification in high-density urban areas of Macau based on disaster scenario simulation

Currently, most of the world's economically developed and densely populated cities are built along coasts, and populations and industries tend to concentrate in coastal areas. However, with climate change, extreme weather events frequently occur in coastal areas. High-density urban spaces have changed the natural hydrological cycle, and the dense spatial layout, low-load municipal system planning, and inefficient management system of coastal cities have led to an increasing threat of flooding in these high-density urban areas, affecting their safety and sustainable development. In this context, the scientific and reasonable risk assessment of future flooding in coastal high-density urban areas has become an urgent problem and a key focus of urban disaster prevention and control efforts. In this paper, a flood identification tool is developed, a technical route for flood risk identification is established, identification scenarios under several disaster conditions are set, and classification criteria for flood risk identification are determined. An empirical study of high-density urban areas in Macau is carried out, the Infoworks Icm hydrological simulation software (Manufacturer: HR Wallingford (Shanghai) Co.,Ltd) is used to establish a flood hazard risk identification model for the Macau Peninsula, and flood hazard risk zones for the high-density urban areas in Macau are identified and delineated based on the simulation results.

Beekeeping Behavior of Chinese Beekeepers Shows Spatial Contraction

Apiculture is an important industry closely related to the national economy and people’s livelihoods. Beekeepers’ behavior is an important factor affecting the yield, quality, and benefits of apiculture. However, there is a lack of a systematic understanding of the long-term changes in beekeeping decisions made by beekeepers. Using panel data, we analyzed the dynamic trends and related influencing factors of decisions made by beekeeping models, honey source plant selection, and the migration flow space of beekeepers from 2009 to 2020. The results showed that the proportion of the LMB model decreased, while the PAB and SMB models continued to increase, the frequency of utilization of the main nectar source plants for honey collection decreased, and the concentration of migratory flow of beekeeping increased. Behavior of beekeepers from 2009 to 2020 showed a certain degree of spatial contraction, which seriously restricted the effective use of nectar plant resources. Family attributes, economic status, beekeeping models, and disaster conditions directly or indirectly affected beekeepers’ decisions. We propose a series of recommendations to facilitate the transformation and advancement of the Chinese bee industry. This study promotes an understanding of sustainable development of the bee industry in China and other countries worldwide.

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

Rainstorm disasters pose a significant threat to the sustainable development of urban areas, and effectively organizing diverse information sources about them is crucial for emergency management. In light of recent advances in knowledge graph theory and application technology, their notable knowledge integration and representation capabilities may offer support for dynamic monitoring and decision-making processes concerning urban rainstorm disaster events. However, conventional models do not adequately capture the spatiotemporal characteristics of these events. To fill this gap, we analyze the essence of urban rainstorm disaster events and divide their evolution into four stages, namely, pregnant, development, continuous, and decline stages. On this basis, a multilevel knowledge representation model is proposed from four layers, which are event, object–state, feature, and relationship layers, by analyzing the components and dynamic characteristics from the mechanism of urban rainstorm disaster events. The proposed model can not only express the comprehensive structure and relationships within urban rainstorm events, but also emphasize the evolution of disaster events through a series of ordered states. Moreover, we test the utility of the constructed knowledge graph through a case study of the Zhengzhou 720 rainstorm. The case study first validates that the selected machine learning models can extract the urban rainstorm disaster event information accurately by comparing them with some mainstream models. Then, it demonstrates that the knowledge graph is practical in the field of disaster knowledge representation, and disaster condition retrieval. Additionally, since the knowledge graph can show the evolution of a disaster event throughout its full life cycle, it can promote the understanding of the mechanisms of urban rainstorm disasters and pave the way for future applications of disaster prevention and reduction.