Disaster Prevention Planning Research Articles

Kilometer-Scale Precipitation Forecasting Utilizing Convolutional Neural Networks: A Case Study of Jiangsu’s Coastal Regions

High-resolution precipitation forecasts play a pivotal role in formulating comprehensive disaster prevention and mitigation plans. As spatial resolution enhances, striking a balance between computation, storage, and simulation accuracy becomes imperative to ensure optimal cost-effectiveness. Convolutional neural networks (CNNs), a cornerstone of deep learning, are examined in this study for their downscaling capabilities in precipitation simulation. During a precipitation event on 23 June 2022, in Jiangsu Province, China, distinct rain belts emerged in both southern and northern Jiangsu, precisely captured by a numerical model (the Weather Research and Forecasting, WRF) with a 3 km spatial resolution. Specifically, precipitation was prevalent in northern Jiangsu from 00:00 to 11:00 Beijing Time (BJT), transitioning to southern Jiangsu from 12:00 to 23:00 BJT on the same day. Upon dynamic downscaling, the model reproduced precipitation in these periods with an average error of 12.35 mm at 3 km and 12.48 mm at 1 km spatial resolutions. Employing CNN technology for statistical downscaling to a 1 km spatial resolution, samples from the initial period were utilized for training, while those from the subsequent period served for validation. Following dynamic downscaling, CNNs with four, five, six, and seven layers exhibited average errors of 8.86 mm, 8.93 mm, 9.71 mm, and 9.70 mm, respectively, accompanied by correlation coefficients of 0.550, 0.570, 0.574, and 0.578, respectively. This analysis indicates that for this precipitation event, a shallower CNN depth yields a lower average error and correlation coefficient, whereas a deeper architecture enhances the correlation coefficient. By employing deep network architectures, CNNs are capable of capturing nonlinear patterns and subtle local features from complex meteorological data, thereby providing more accurate predictions during the downscaling process. Leveraging faster computation and reduced storage requirements, machine learning has demonstrated immense potential in high-resolution forecasting research. There is significant scope for advancing technologies that integrate numerical models with machine learning to achieve higher-resolution numerical forecasts.

Numerical simulation on the prevention and control of landslide-induced wave disasters in Wangjiashan landslide area of Baihetan reservoir

The risk management of landslide surges after water storage in large reservoirs is a major challenge in reservoir management. The water storage in the reservoir area of the Baihetan hydropower station on the Jinsha River brings the potential of Wangjiashan landslide activation. This paper proposes a mitigation measurement based on slope cutting for the Wangjiashan landslide and evaluates the feasibility and effectiveness of the management scheme through numerical simulation. Results indicate that when water storage is in a normal level, after an earthquake, the landslide will impact the Baihetan Reservoir with a maximum velocity of 8.52 m/s, generating waves with a maximum wave height of 15.63 m. By removing the upper part of the landslide, the volume of the landslide is reduced from 611 × 10 4 m 3 to 369 × 10 4 m 3 , it will cut down the maximum wave height in the Xiangbiling residential area to 3.09 m, which significantly reduces the risk of landslide surge waves. It further establishes a disaster prevention and control plan for the landslide-induced wave disaster. Overall, this study provides important theoretical and practical references for the risk management of landslide surge waves and offers valuable insights for addressing similar issues in the future.

Linking affected community and academic knowledge: a community-based participatory research framework based on a Shichigahama project

Earthquakes that cause extensive damage occur frequently in Japan, the most recent being the Noto Peninsula earthquake on January 1, 2024. To facilitate such a recovery, we introduce a community-based participatory research program implemented through cooperation between universities and local communities after the 2011 Great East Japan Earthquake. In this project, the university and the town of Shichigahama, one of the affected areas, collaborated to hold annual workshops in the target area, which evolved into a climate monitoring survey. Even in Japan, where disaster prevention planning is widespread, various problems arise in the process of emergency response, recovery and reconstruction, and building back better when disasters occur. As is difficult for residents and local governments to solve these problems alone, it is helpful when experts participate in the response process. In this study, we interviewed town hall and university officials as representatives of local residents regarding this project and discussed their mutual concerns. The community-based participatory research framework developed in the Shichigahama project could be used in the recovery from the Noto Peninsula Earthquake as well as in future reconstruction and disaster management projects.

Evaluating the Influence of Sustainable Urban Disaster Prevention Planning Satisfaction on Residents’ Living Willingness: A Structural Equation Modeling Approach in Kaohsiung City

Rapid urban development and increasing climate-related challenges underscore the need for effective urban disaster mitigation strategies. This study, conducted in Kaohsiung City, Taiwan, uses a Structural Equation Model to assess how satisfaction with urban disaster prevention planning influences residents’ intentions to stay there. We found that satisfaction with sustainable urban disaster prevention increases residents’ willingness to remain and acts as a key mediator between satisfaction with public facilities and residential intentions. The methodology provides practical insights for urban planners and policymakers, highlighting that improving public facilities and effective disaster planning significantly strengthens residents’ attachment to urban environments. These findings suggest a pragmatic way to enhance urban sustainability and resilience by incorporating disaster preparedness into urban development plans.

A demand-based three-stage seismic resilience assessment and multi-objective optimization method of community water distribution networks

This paper proposes a three-stage resilience concept that takes into account reliability, vulnerability and recovery resilience of water distribution networks (WDNs) from three phases, namely, pre-disaster preparation (PDP) phase, disaster response (DR) phase and the post-disaster recovery (PDR) phase. Based on the three-stage resilience concept, the initial performance, robustness and multi-objective optimization recovery method are proposed. In PDP phase, based on the reliability concept and the user demand, the initial performance of WDNs is defined, and the method is proposed based on Monte Carlo simulation method combined with seismic fragility of pipelines. In DR phase, a comprehensive quantitative index is proposed to quantify the robustness of WDNs by integrating the structural attributes, topological attributes and operational characteristics of WDNs. Finally, on the basis of the previous two stages, the disaster recovery process of failed pipelines of WDNs is further analyzed, and a multi-objective recovery optimization model of failed pipelines with consideration of users’ demands under the resources constraints is proposed to achieve the resilience enhancement. Through a case, the three-stage resilience assessment optimization enhancement of WDNs is implemented using the improved NSGA-II. The proposed method can be used as a basis for disaster prevention planning and seismic measures.

A clustering-based analysis method for simulating seismic damage of buildings in large cities

Earthquakes are major threats to cities. Refined simulations of urban earthquake damage are significant for disaster prevention planning, including post-earthquake emergency rescue operations. However, this approach is time-consuming and computationally demanding. This study presents a novel machine learning method for simulating seismic damage of large urban building groups, thus minimizing the computational time and resources associated with the simulation process. First, the proposed method clusters building structures based on their seismic damage index (DI) under specific ground motions. Subsequently, one structure is selected from each cluster for seismic analysis. This approach significantly reduces the number of analyzed structures. The efficiency and accuracy of this method are studied based on a parametric study that involved an improved K-means clustering algorithm and a grid-based clustering algorithm. A batch-clustering algorithm is also developed to further improve the speed of clustering and efficiency of regional seismic simulations. A large Chinese city is considered as research application example. The results indicated the following: (1) the method based on the improved K-means clustering algorithm was superior; (2) the batch-clustering algorithm significantly speeded up clustering analysis and enhanced efficiency of the seismic simulation; (3) the clustering-based simulation method demonstrated high efficiency and accuracy, with a significant decrease of 90.5% in the calculation time compared with direct nonlinear time history analysis. In addition, the average relative error of the DI was only 11.0%. Moreover, over 85.0% of the structures were estimated to be in the correct damage state.

Unravelling the Economic Impacts

This study aimed to reveal the macroeconomic effects of the earthquakes that occurred in Türkiye in February 2023 on the country as a whole, the affected region (covering 11 provinces), and other provinces. Using secondary data obtained from data sets of various institutions, this research firstly attempted to estimate the negative effects of the February Earthquakes on the country’s GDP. The study presents a base scenario using the economic growth forecasts of international organizations such as the IMF and the World Bank for 2023 and 2024 as well as three possible loss scenarios for Türkiye, taking into account the effects of earthquakes of similar magnitude on the GDP in Türkiye and other countries. Furthermore, this study tried to estimate the losses caused by earthquakes in terms of employment, GDP, agriculture, industry, services, and foreign trade at the regional and provincial levels. To do this, we created the Base Scenario, which assumes no earthquake, and Scenario 1, which shows the impact of the earthquake. The findings indicate that, in the worst-case scenario, the country will contract by approximately 2.75%, which means a loss of $8.8 billion. Moreover, the study results point out an average monthly loss of 242 thousand jobs, a loss of $5 billion in GDP, an export loss of $3.5 billion, and an import loss of $4.7 billion in the 11 earthquake-affected provinces. The research underlines the need for comprehensive measures to mitigate the economic consequences caused by earthquakes. This includes disaster prevention plans, effective management strategies and initiatives aimed at strengthening regional economic resilience. Ultimately, the article provides valuable information for policymakers to facilitate informed decisions and the implementation of measures to increase preparedness, risk mitigation, and sustainable recovery in earthquake-prone areas.

Towards Resilient Cities: Optimizing Shelter Site Selection and Disaster Prevention Life Circle Construction Using GIS and Supply-Demand Considerations

City health examinations are integral to China’s urban planning, construction, and management. They effectively identify potential risks and vulnerabilities in urban development, ensuring safety resilience—a critical component. This resilience enhances the city’s ability to withstand internal and external shocks, promoting the safety of urban residents and fostering sustainable city development. Drawing on the Japanese disaster prevention strategy, the disaster prevention life circle emerges as a rescue and protection system during urban disasters, fortifying urban safety resilience. However, smaller and mid-sized cities, constrained by limited resources, significantly need to catch up in disaster prevention planning. Consequently, bolstering safety resilience in these cities becomes a pressing concern. This study focuses on Lindian County in Heilongjiang Province as the urban area under consideration for resilient city objectives. Leveraging the ArcGIS network analysis tool, we optimize the placement of emergency shelters, aligning with urban disaster assessments and the equilibrium of disaster prevention facility supply and demand. Accessibility analysis of emergency shelters was conducted using the Gaussian two-step floating catchment area method. Ultimately, we integrate the range of demand points assigned to each shelter, along with the effective land area reflecting the supply of shelters, as weights into a weighted Voronoi diagram. This diagram is combined with a reference to the entire region to delineate the disaster prevention life circle. Findings reveal that, under the premise of minimizing government construction costs while maximizing coverage and evacuation utilization rates, the optimal resident emergency congregate shelters in the study area are 8, with 98 emergency evacuation and embarkation shelters. Striking a balance between disaster prevention facility supply and demand and regional accessibility, the urban area of Lindian County is segmented into 3 resident disaster prevention life circles and 24 emergency disaster prevention life circles. The objective of this study is to optimize shelter siting and establish disaster prevention life circles in diverse urban areas. This endeavor aims to bolster urban resilience and foster sustainable urban development.

Implementing A resistance-relief approach into evaluating urban disaster management capacity: A case study of Xuzhou

In the field of disaster management, urban planning is a crucial tool for reducing the vulnerability of areas prone to natural disasters. Assessing the disaster management efficacy of urban planning is essential. However, the theoretical evaluation frameworks and precise assessment methods currently available are quite limited. Therefore, this study developed a new Resistance-Relief (2Rs) approach to evaluate disaster management capabilities in different regions. Based on the “2Rs” evaluation framework, an indicator system was developed to assess disaster management capabilities at the level of urban planning and construction. On this basis, a directed weighted graph model was established to comprehensively evaluate a city's disaster resistance and disaster relief capabilities, addressing the accuracy and comprehensiveness issues commonly found in single-dimensional evaluations. Furthermore, an indicator sensitivity analysis method based on the cloud model was proposed to analyze the sensitivity of evaluation indicators and the robustness of evaluation results. Using actual urban planning and construction data, this method was applied to assess seven disaster management zones in Xuzhou City, China. The results revealed inadequacies in the city's disaster preparedness and mitigation construction, as well as key factors affecting Xuzhou's disaster management capabilities. This new method contributes to further improvements in both disaster risk management and urban disaster prevention and mitigation planning.

Does Economic Growth Increase or Decrease Economic Losses and Deaths from Storm Surge Disasters in China? An Empirical Analysis from The Aspect of Hazard Exposure

Storm surge disaster is the major threat to coastal residents’ lives and property in China, which could steal away development gains in a short time. Therefore, the storm surge disaster loss reduction is an indispensable part of socio-economic development. However, the relationship between economic growth and the loss of storm surge disasters remains unclear. Herein, we explored the relationship between economic growth and the losses caused by storm surge disasters in China’s coastal zones from 1978 to 2020. We established an empirical model through the indicators of direct economic loss and death toll using negative binomial estimate and tobit estimate models. To ensure the accuracy of the model, multiple socioeconomic factors were also included in the models. The results revealed that there is an “inverted U-shaped” relationship between GDP per capita and the losses from storm surges. Furthermore, we used the entropy method to establish the storm surge hazard exposure index of China’s coastal zone from 2000 to 2020, considering disaster intensity, population density and other factors. When considering hazard exposure level, the role of economic growth may be diminished. In other words, at the same economic level, higher hazard exposure leads to greater disaster losses. We argue that economic growth and disaster resilience are not identical goals. Thus, the government needs sufficient information support when formulating disaster prevention and reduction plans.

Simulation Study on Rain-Flood Regulation in Urban “Gray-Green-Blue” Spaces Based on System Dynamics: A Case Study of the Guitang River Basin in Changsha

Urban rainstorms and flood disasters are the most common and severe environmental problems worldwide. Many factors influence rain-flood control simulation, forming a complex network system of interconnected and mutually constraining elements. In terms of spatial scale selection, existing research on rain-flood disaster risk largely relies on a single-scale infrastructure index system and has not yet focused on urban “gray-green-blue” spatial scale simulations for rain-flood storage. Regarding research methodology, applying system dynamics methods to the simulation of rain-flood storage and disaster prevention planning in watershed cities is still in its initial stages. System dynamics models can simulate the feedback interactions among various sub-elements in the coupled mega-system, fully addressing complex issues within the system structure that involve multiple variables, non-linear relationships, and numerous feedback loops, thereby compensating for the inadequacies of traditional linear models in the collaborative management of rain-flood risks. Taking the Changsha Guitang River Basin as an example, this paper constructs a system dynamics model covering four dimensions: natural environment, socio-economics, internal structure, and policy development. It aims to derive the optimal planning scheme for gray-green-blue spatial coordination in rain-flood storage by weighing four different development scenarios. The simulation results show: (1) Simply changing the surface substrates without considering rainwater discharge and the plan that emphasizes the construction of municipal drainage facilities will see the capacity gap for rain-flood storage-space construction continue to widen by 2035. This indicates that the plans mentioned above will struggle to bear the socio-economic losses cities face during rain-flood disasters. (2) The plan of combining gray and green infrastructures sees the rain-flood storage construction capacity turn from negative to positive from 2024, rising to 52.259 billion yuan by 2035. This reflects that the plan can significantly reduce the rainwater volume in the later stages of low-impact development infrastructure construction, mitigate rain-flood disaster risks, and reduce government investment in rain-flood disaster risk management, making it a relatively excellent long-term rain-flood storage space planning option. (3) The rain-flood regulation space planning scheme, under the combined effect of the urban “gray-green-blue” network system, sees the capacity for rain-flood storage construction turn positive a year earlier than the previous plan, reaching 54.232 billion yuan by 2035. This indicates that the scheme can not only effectively respond to extreme flood and rainstorm disasters but also maintain ecological environment benefits and mitigate the socio-economic losses caused by disasters, making it the optimal choice for future government disaster management planning. The research results provide a theoretical framework and practical insights for territorial spatial planning, rain-flood control management, and resilient city construction in watershed areas.

Flood economic vulnerability and risk assessment at the urban mesoscale based on land use: A case study in Changsha, China

With climate change and urbanization, flood disasters have significantly affected urban development worldwide. In this study, we developed a paradigm to assess flood economic vulnerability and risk at the urban mesoscale, focusing on urban land use. A hydrological simulation was used to evaluate flood hazards through inundation analyses, and a hazard-vulnerability matrix was applied to assess flood risk, enhancing the economic vulnerability assessment by quantifying the differing economic value and flood losses associated with different land types. The case study of Wangchengpo, Changsha, China, found average total economic losses of 126.94 USD/m2, with the highest risk in the settlement core. Residential areas had the highest flood hazard, vulnerability, and losses (61.10% of the total loss); transportation areas accounted for 27.87% of the total economic losses due to their high flooding depth. Despite low inundation, industrial land showed greater economic vulnerability due to higher overall economic value (10.52% of the total). Our findings highlight the influence of land types and industry differences on flood vulnerability and the effectiveness of land-use inclusion in urban-mesoscale analyses of spatial flood characteristics. We identify critical areas with hazard and economic vulnerability for urban land and disaster prevention management and planning, helping to offer targeted flood control strategies to enhance urban resilience.

Insights and viewpoints from a small-scale survey on current nuclear disaster preparedness measures in Japan after the Fukushima Daiichi Nuclear Power Plant accident

In nuclear disaster prevention, it is essential not only to make daily efforts to prevent accidents from occurring but also to properly apply lessons learnt from actual disasters. Although significant changes have been made to nuclear disaster preparedness in Japan since the Fukushima Daiichi Nuclear Power Plant accident, there is insufficient information on whether these changes have been evaluated as practical and appropriate for the needs of the Japanese public. In this survey, 20 officials of the Cabinet Office and Japan Atomic Energy Agency, in charge of planning nuclear disaster prevention policy, were asked to evaluate the current nuclear disaster prevention plan through a questionnaire, and compare it with that before the accident, and indicate what elements are lacking in the current plan. The survey results revealed that 30% of the participants (six respondents) had a positive view of the enhancement of resources, including physical and human assets. However, as many as 60% (12 respondents) expressed negative sentiments, primarily due to perceived deficiencies in organisational measures, particularly the coordination of these resources. Moreover, the participants expressed keen interest in obtaining health data during evacuation, along with information on the physical and mental effects on evacuees living in evacuation centres. These crucial insights can inform the formulation of effective future preparedness plans for evacuation and radiation protection.

Arrangements for Telephone Consultation on Radiation Health Effects in a Nuclear Emergency in Japan: Lessons Learned from the Nuclear Accident in Fukushima, Japan, 2011

The Japan Atomic Energy Agency (JAEA) established a telephone consultation system at the request of the Ministry of Education, Culture, Sports, Science and Technology in response to residents’ concerns about the radiation health effects following the nuclear accidents at the Fukushima Daiichi Nuclear Power Station of the Tokyo Electric Power Company (TEPCO) in March 2011. Eight toll-free telephone lines were established and employees with knowledge of radiation health effects were assigned for consultation. The JAEA expert response group for telephone inquiries comprised a telephone response team, a question-and-answer team, and a manager. Approximately 35,000 consultations were conducted from March 17, 2011 to September 18, 2012. Japan’s Basic Disaster Prevention Plan was revised following the accident, where one of the JAEA’s roles for nuclear emergency response was to provide telephone consultation on radiation health effects. The JAEA’s system for telephone consultation was improved following this experience. The description of telephone hotlines in the International Atomic Energy Agency’s guide, GSG-14, published in 2020, was discussed for further improvement in the JAEA’s telephone consultation system.

A study on compound flood prediction and inundation simulation under future scenarios in a coastal city

Coastal cities are affected by the compound effects of flood hazard systems, including the ocean, river, and coastal land, within which the disaster-induced factors interact complexly. To explore the combined effects of multiple disaster-causing factors under changing environment, this study first constructed univariate distribution by maximum daily precipitation of the year at Zhuhai Station from 1962 to 2020 and maximum daily average river discharge of the year at Makou Station from 1951 to 2005. Based on the integrated model of the validated Delft3D-FLOW and HEC-RAS hydrodynamic models, the river level process of compound floods was simulated for 24 h, and the effects of extreme compound flood events involving storm surge, rainfall and river flood under future scenarios is analyzed. In this study, the Modaomen waterway in Zhuhai City of China is used as the study area. The results show: (1) The Lognormal distribution is selected as the optimal distribution, and the design values of precipitation and discharge in different recurrence periods are calculated based on Lognormal distribution. The precipitation and discharge for the 100-year return period were 450.3 mm and 51000 m3, respectively, which could be used in the compound flood simulation; (2) The application of Delft3D-FLOW model ensures the accurate simulation of the compound flood process. In the different future scenarios of rising sea levels and increasing wind intensity (WI), the effect of multiple disaster-causing factors results to a more serious inundation than that of a single disaster-causing factor, with the increase of maximum storm surge level from 8.09 % to 20.31 %; and (3) Fluvial flooding and storm surge became the major causes of compound flood when the city coastal protection is vulnerable, while in the city with robust coastal protection, precipitation serves as a main disaster-causing factor. Taking the inundation under scenario S1 as an example, when the seawall elevation is 2 m, the composite flooding causes significant inundation in the riparian area (the total inundation area is 15.29 km2), whereas when the seawall elevation is raised to 2.5 m, the inundation area in the riparian area caused by the compound flood decreases significantly (the total inundation area is 0.03 km2). This study is expected to provide scientific references on disaster prevention planning, compound disaster risk management and urban resilience construction along the coastline, as well as have a significant impact on modern economic growth, social stability, and the security in coastal cities.

A Multi-Hazard Risk Assessment Framework for Urban Disaster Prevention Planning: A Case Study of Xiamen, China

Understanding and measuring the relative risk level of a city facing multi-hazards is fundamental to improving its disaster prevention planning and schemes. A comprehensive risk evaluation approach stands at the intersection of risk management and disaster system theory. It is also an important interdisciplinary field of catastrophology, economics, and urban infrastructure planning. We believe that current attempts to define and measure comprehensive urban natural disaster risks have certain limitations. Therefore, we propose an Urban Multi-hazards Risk Assessment Framework (UMRAF) which draws on definitions, methods, and experience from risk management, evaluation of property, the value of statistical life, and disaster system theory. It contains local disaster identification, place-based risk assessment (taking into account more than one hazard at a time), urban anti-disaster capability assessment, and relative composite risk index measurement. In our case study of Xiamen, China, to check the feasibility of our UMRAF, we examined local multi-hazards risk distribution and urban anti-disaster capacity layout. We then expanded and visualised the spatial distribution of the relative composite risk index of each evaluation unit across the city via our analyst tool, thereby helping to tailor measures that can reduce risk at a local level.

Automatic identification of building structure types using unmanned aerial vehicle oblique images and deep learning considering facade prior knowledge

ABSTRACT Building structural type information is the foundation for seismic risk assessment and management since it reflects the behavior of buildings under seismic load. However, in earthquake-prone regions, most of this information is out-of-date or nonexistent. This paper proposes a deep learning-based method for automatically identifying building structural types from unmanned aerial vehicle (UAV) oblique images. The method consists of four steps: (1) collect facades of buildings with different structural types by web crawler technology as a sample set; (2) construct a convolutional neural network with a facade prior knowledge attention branch and train the model using the sample set; (3) extract building facades from UAV oblique images based on the georeferencing results of feature points as the test set; (4) identify building structural types by inputting the test set into the trained model. Three cases have been selected to verify the feasibility and applicability of the method. The average recall rate of 85% and the average F1 score of 83% have been achieved in areas with regular building distribution. This method integrates multidisciplinary knowledge to provide a solution for rapid collection of building vulnerability information, and expands the role of oblique photography data in urban management and disaster prevention planning.

Evaluation and formation mechanism of rural social vulnerability under natural hazard: A case study of the eastern Qinghai-Tibetan Plateau, China

Global environmental change has posed serious threats and challenges to human survival and sustainable development, especially for mountainous communities with fragile ecological environments, weak infrastructure, and underdeveloped economic development. Reducing rural social vulnerability has become an important way to promote global rural revitalization and achieve the United Nations Sustainable Development Goals. However, current research has mostly focused on social vulnerability assessment, with few revealing its formation mechanism. Based on the collected survey data of 240 households, this paper selected Zhouqu county as a case, which located in the eastern edge of the Qinghai-Tibet Plateau, and constructed theoretical framework and hypothesis model of social vulnerability from three dimensions: exposure, sensitivity, and adaptability. On the basis of analyzing the characteristics of social vulnerability, we used structural equation model to further explore its formation mechanism. The result shows that the mean value of social vulnerability index of farmers under natural hazards is 0.442, and nearly 30% of the surveyed farmers have high social vulnerability. There are significant differences in social vulnerability among households in different communities, different economic levels and different livelihood styles. Natural hazards have a double-edged sword effect on the rural social system. They can not only directly aggravate social vulnerability by weakening household natural capital, physical capital, financial capital and psychological capital, but also indirectly stimulate the vitality of social capital, integrate human capital and social resource to reduce social vulnerability. Finally, we put forward effective countermeasures to reduce the rural social vulnerability. Our study aimed to providing theoretical reference for developing countries to formulate risk management policies and disaster prevention and mitigation planning in mountain areas.

Constructing an adaptability evaluation framework for community-based disaster management using an earthquake event

A community's disaster resilience and response capabilities are highly related to citizens' participation in disaster prevention planning and management. Therefore, community-based disaster management (CBDM) systems should integrate citizen participation and community capital under the category of community resilience, which has been recognized as a key aspect of disaster management. Using an earthquake event as a case, this study proposes an adaptability evaluation framework for CBDM integrating community resilience and capital. Based on interviews with stakeholders and a choice experiment method, we estimate a resident preference model under the CBDM framework. This study also measures resident preference heterogeneity using demographic data and prevention attitudes and estimates the welfare effects of CBDM. Finally, we evaluate residents' willingness to participate in different CBDM scenarios in an earthquake event. The results confirm that residents prefer holding regular disaster prevention drills, the organization of cooperative drills with local governments, the provision of disaster prevention training, and the channeling of both non-government organizations and government resources in response to a disaster. Significantly, in terms of disaster prevention measures, the integrated community disaster management framework garnered the highest willingness to participate among all scenarios, followed by enhancing residents' adaptive ability and focusing on residents' awareness and perceptions. These findings provide valuable insights into the theoretical design and construction of CBDM systems for earthquake events and related policymaking.

A research on data integration and application technology of urban comprehensive pipe gallery based on three‐dimensional geographic information system platform

AbstractThree‐dimensional Geographic Information System (3D GIS) technology is often used in geology, mining, hydrology and other fields. It is also used in intelligent management of urban comprehensive pipe gallery projects, because the real environment with the sky, ground, and underground can be intuitively simulated in the virtual reality environment by 3D GIS platform. Through the research on data integration and application technology of urban comprehensive pipe gallery, the 3D model of comprehensive pipe gallery project data can be created based on Building Information Modelling and GIS technologies, and a comprehensive underground pipelines network management information system has been developed to realise the integration of various data, including 3D data of comprehensive pipe gallery model and underground pipe network model, 2D or 3D GIS data of the ground and underground etc. On this basis, the functions of intelligent planning and approval management, the safety analysis and disaster prevention planning and decision‐making of urban comprehensive pipe gallery projects can be further realised. The research results have been applied to the planning and construction process management of the comprehensive pipe gallery project in the sub‐centre of Beijing.