基于风险分析的城市应急设施选址研究
文献类型：学位论文
| 作者 | 赵明 |
| 学位类别 | 博士 |
| 答辩日期 | 2015-05 |
| 授予单位 | 中国科学院研究生院 |
| 授予地点 | 北京 |
| 导师 | 陈求稳 |
| 关键词 | 突发环境事故，城市公共安全，应急设施选址，区域风险评价，多目 标优化，决策支持系统，environmental accidents, urban public safety, emergency facilities locations, regional risk assessment, multi-objective optimization, decision support system.。 |
| 其他题名 | Risk-based optimization of emergency rescue facilities locations in large-scale urban areas |
| 学位专业 | 环境工程 |
| 中文摘要 | 快速城镇化带来了经济的繁荣，但随之而来的城市公共安全问题也日益突出。在城市应急管理工作中，对于自然灾害和突发事件的应对，往往最终要落实到应急资源的管理和使用上来。其中，城市应急设施的合理选址对于发挥应急资源的最大效益至关重要，在应急管理的资源保障工作中起着关键的作用。因此，研究有效的应急设施选址策略对于城市减灾和城市公共安全保护具有重要意义。 本研究首先针对城市应急救援的特点，构建了应急设施选址的多目标决策模型，设计了适合的空间表征和算法编码方法，并使用多目标进化算法对模型进行了求解。同时，对现有的风险评价方法进行了改进，提出了一种基于严重度和脆弱性的城市重大危险源区域风险评价方法。在此基础上，将风险评价的模型与城市应急设施选址模型进行耦合，以满足城市突发环境污染类事故应急救援的实际需要。最后，以实际案例的形式检验了上述方法的可行性和有效性，并设计和开发了一款专用的决策支持工具，以支持决策者获得城市应急设施的 最优选址方案。 本论文的主要结论包括： 1、建立了一种用于解决城市应急救援设施选址问题的方法。针对重大突发事件的特点，将全局救援能力、全局救援效率和设施空间分布公平性作为决策目标，构建了应急救援设施选址多目标决策模型。根据模型的特点，设计了基于网格的空间表征方法和算法编码策略，并使用多目标进化算法对模型进行了求解。最后，以实例验证了此方法的可行性和有效性。研究结果表明，此方法框架可以满足城市应急设施选址规划的需要，能够为决策者制定选址决策提供有效的候选方案。 2、提出了一种城市重大危险源区域风险评价方法。针对城市应急管理的特点，对风险严重度计算和脆弱性评估进行了修订，并利用GIS 技术，对严重度和脆弱性进行叠加分析，绘制出城市重大危险源区域风险地图。实例分析表明，此方法为快速获取城市重大危险源区域风险的空间分布格局提供了新思路，对于降低城市突发环境污染事故的影响和辅助决策者制定科学的城市公共安全管理决策具有一定的实际意义。 3、建立了基于风险分析的城市应急设施选址策略。将风险分析方法引入到应急设施选址模型，满足了城市突发环境污染事故应急救援的实际需要。实例分析表明，基于风险分析的应急设施选址策略能成功获得有效的候选解决方案。同时，风险分析方法的引入可以显著地影响应急设施选址的结果，为提升城市应急救援能力和辅助决策者制定科学的城市公共安全管理决策提供了支持。 4、设计并开发了一款基于GIS 的城市应急设施选址决策支持工具。设计了系统的概念模型，确定了系统的架构和技术解决方案，开发了相关功能模块和图形用户界面。应用表明该工具能够有效地为决策者提供多种候选选址方案，在改善城市应急管理水平方面具有较好的价值。 |
| 英文摘要 | Urbanization has brought economic prosperity, but with urban public security issues are also increasingly prominent. Urban emergency management often ends up in the management and use of emergency resources. Emergency facility is an important component of urban system, and the locations of these emergency facilities are crucial for ensuring the efficiency of emergency relief distribution as well as casualty transportation. Due to the special characteristics and complex environment of large-scale environmental disasters in urban areas, locations of emergency rescue facilities should be optimized. Consequently, developing strategies for effectively optimizing these emergency rescue facilities locations (ERFLs) is of great significance for disaster mitigation and public safety protection. In this thesis, firstly, we proposed an innovative methodology for solving ERFLs problem in the context of large-scale urban disaster. Both the framework of decision model construction and the model solution strategy were presented. Specifically, a three-objective decision optimization model was constructed, and an appropriate spatial presentation and encoding strategy was designed and coupled with the NSGA-II algorithm for model solution. Secondly, in the light of special characteristics of urban emergency management, we proposed a revised regional risk assessment method for urban major hazards in this study. And then we involved the risk assessment method into the methodology above, in order to extend its application scope to environmental accidents emergency situation. Finally, we designedand developed a scalable decision support tool based on geographic information system (GIS) to facilitate optimizing ERFLs in large-scale urban areas. The major conclusions of this study are as follows: 1) Established the methodological framework to optimize the ERFLs. A decision optimization model which consists of three-objectives representing service capacity, global efficiency and equity was developed. An appropriate spatial representation and encoding strategy was designed and coupled with the NSGA-II algorithm for model solving. Based on a hypothetical disaster scenario, a case study was presented to demonstrate the methodology. The results provide evidence that the model is able to successfully generate the Pareto-optimal frontier for the multi-objective ERFLs problem, and provide a pool of alternative solutions to the decision-makers. The findings show that the framework proposed in this study has the potential to be a useful decision support tool for urban planning with respect to public safety. 2) Proposed a revised regional risk assessment method for urban major hazards.In the light of special characteristics of urban emergency management, based on ARAMIS methodology, Acute Exposure Guideline Levels (AEGL) were involved to calculate the severity index and delineated the ranges of each threat zone. Moreover, indicators related to the efficiency of emergency rescue service were identified and weighted into the vulnerability assessment of each mesh. Finally, the risk mapping was carried out by overlapping severity and vulnerability of each mesh based on GIS technology. The results of a case study provide evidence that the method proposed is able to successfully obtain the regional risk pattern of urban major hazards. The findings also show its potential to enhance urban public safety management as well as disaster mitigation. 3) Established the risk-based strategy for optimizing ERFLs. Risk-based approach was involved into the methodological framework above to delineate the potential hazard of major hazardous sources to the adjacent areas, in order to extend its application scope to large-scale urban environmental accidents emergency situation. A case study was also presented to demonstrate that the involvement of risk mapping into the model construction was significant to the optimization result. 4) Designed and developed a GIS-based decision support tool as a direct outgrowth of our previous work. The tool was designed to help the users to find optimum ERFLs in large-scale urban areas, and we described the design, architecture, implementation of the tool. Based on a hypothetical disaster scenario, we introduced its functionalities as well as the decision making workflow. The findings demonstrate the potential of such a novel tool in facilitating urban emergency management research. In addition, this work offers new insights on enhancing future GIScience research with the use of emerging artificial intelligence technologies, and make contributions in integrating multi-objective optimization with GIS for the next frontier of geospatial optimization in Big Geo-Data age. |
| 源URL | [http://ir.rcees.ac.cn/handle/311016/34481]  |
| 专题 | 生态环境研究中心_环境水质学国家重点实验室 |
| 推荐引用方式 GB/T 7714 | 赵明. 基于风险分析的城市应急设施选址研究[D]. 北京. 中国科学院研究生院. 2015. |
入库方式： OAI收割
来源：生态环境研究中心
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