基于不确定性的水资源优化配置模型及其实证研究

针对城市水资源系统中存在的不确定性,将可信性模糊机会约束规划模型与区间规划相结合,提出了不确定环境下的可信性模糊-区间线性规划(FILP)模型,将其应用于某城市水资源优化配置与科学管理中,构建了城市水资源优化配置FILP模型.该模型以城市的经济、社会与环境的可持续发展为目标,以供需水量等为不确定性约束,以各水源在各子区不同部门间的分配为决策变量,利用改进的风险显性区间规划算法进行求解得到一定置信度和意愿水平下的水资源优化配置风险-收益权衡方案.实证研究表明,该模型反映了不确定性因素对水资源系统收益的影响,能够为实际的优化决策提供方法支持.     

Minimax regret discounting

The paper considers an environmental policy decision in which the appropriate approach for discounting future costs and benefits is unknown. Uncertainty about the discount rate is formulated as a decision under Knightian uncertainty. To solve this, we employ minimax regret, a decision criterion that is much less conservative then the related criterion maximin—in particular, it can be shown to implement a “proportional response” in that it equally balances concern about the mistake of doing too little with that of doing too much. Despite the criterion's balanced nature, the minimax regret solution mimics a policy that maximizes the present discounted value of future net benefits with an effective (certainty-equivalent) discount rate that declines over time to the lowest possible rate. In addition to reinforcing Weitzman's (1998) original limiting result, the approach generates concrete policy advice when decision makers are unable to specify a prior over possible discount rates. We apply it to the Stern–Nordhaus discounting debate and find that the effective discount rate converges to the Stern rate in just under 200 years.     

橡胶拉伸强度的测量不确定度评定解析

本文在介绍测量不确定度的概念、分类和评定方法的基础上,以橡胶拉伸强度的测试为例,对如何评定破坏性试验的测量不确定度进行了有益的尝试,并详细阐述了测量不确定度评定方法的具体应用。     

毒气泄漏扩散风险的参数不确定性分析

有毒气体泄漏扩散受很多不确定性因素的影响,为了分析和评估影响毒气泄漏扩散的风速和泄漏速率的变化和不确定性,采用蒙特卡罗模拟和基于Wilks公式容许限的非参数统计法,通过抽样计算得到“95/95准则”下的毒气泄漏扩散地面浓度分布,计算了有毒气体泄漏扩散的不同风险等级的影响范围和风险概率曲线。以氨气泄漏事故为例进行实例分析,结果表明,相对于以确定性参数得出的氨气泄漏扩散浓度分布,引入参数的不确定性评估,更能贴合泄漏现场存在不确定性因素的实际情况,更有利于人员的安全和应急疏散管理。     

多应急点多需求点物资调度的灰色多目标规划

为了研究在信息不确定情况下,应急物资需求量和应急时间为区间数的物资调度问题,基于灰色系统理论构建多目标规划模型,结合实例并采用灰色区间数求解方法,计算物资调度方案。结果表明:应急物资调度与区间参数取值无关,相关调度模型与算法对应急初期的实际物资调度具有参考意义;运用灰色区间数描述调度中的不确定因素,有助于提高实际物资调度过程中的准确性。     

基于Monte Carlo与Markov法参数不确定性条件下SIL评估

为了避免由参数不确定性因素导致较大的Markov法SIL评估偏差,且减少计算工作量和复杂性,采用Monte Carlo（MC）仿真方法处理含不确定性参数的Markov模型,并借助Matlab GUI编程开发MC仿真处理参数不确定性条件下Markov法SIL评估可视化仿真计算软件。在理论研究的基础上,为说明该研究方法与计算软件的可行性,以石油天然气工业高完整性压力保护系统（HIPPS）为算例进行SIL评估。结果表明:MC仿真方法可以有效处理Markov法SIL评估中参数不确定性问题;基于Matlab GUI编程设计出的仿真计算软件在一定程度上可以提高计算效率。     

多因素耦合作用下建筑火灾后加固施工过程风险性评估

为降低火灾后建筑结构加固施工过程中事故的发生率,保障加固施工作业过程的 安全,针对我国建筑火灾后加固施工项目的特点,构建了多因素耦合作用下的基于熵权 -未确知测度理论的建筑火灾后加固施工过程风险性评估模型,并将模型应用于五个工 程实例中,取得了良好的效果。研究结果表明,该模型是熵权理论、未确知测度理论的 的有机结合,为定量研究建筑火灾后加固施工过程安全风险,并有效的控制安全风险, 确保施工安全提供了新的思路。     

东莞市某地区地表水中重金属健康风险不确定性评价

为了解东莞市西南地区(麻涌镇、沙田镇、虎门镇和长安镇)地表水中重金属污染现状与健康风险水平,对研究区域内29个地表水样中As、Cd、Cr、Cu、Hg、Ni、Pb和Zn等重金属元素进行测定,并运用不确定性健康风险评价模型对研究区地表水中的重金属进行健康风险评估。结果表明:该研究区域地表水中重金属引起的致癌风险较高,人均年健康风险都在Ⅴ级风险及以上。化学致癌性重金属引起的人均年健康风险中,以Cr最大,As次之,然后是Cd。非化学致癌性重金属引起的人均年健康风险中,Pb的风险最大,Ni次之,风险均远小于化学致癌性重金属引起的人均年健康风险。因此,该研究区域地表水中重金属健康风险管理应以化学致癌风险为主,重金属污染物的优先控制顺序为CrAsCd。     

Climate change implications for disaster risk management in Japan: A case study on perceptions of risk management personnel and communities in Saijo city

This paper reviews climate change impacts and the existing disaster risk management system in Japan and offers the results of a structured questionnaire survey of the community leaders and disaster risk management personnel of Saijo city of Japan that assesses their perceptions about dealing with the extreme disasters by the existing disaster risk management systems. This study was inspired by the record number of typhoon landfall that has surprised the local government and communities in 2004. While unearthing the hidden vulnerabilities in cities like Saijo, this event has loosened the confidence of local communities on the disaster risk management systems. From the study, we conclude that the existing disaster risk management systems need further fillip and that the proactive community involvement in disaster risk management is still in nascent stages. Associating with the scientific community, involving the local communities (including the elderly), enhancing the redundancy in disaster risk management systems, inculcating strategic thinking and micro-level planning, conducting vulnerability assessments by considering the special circumstances including resource constraints of small cities and better policy coordination across the administrative hierarchy are some important considerations for dealing with the uncertainty brought by the extreme events.     

Scaling up of biomass simulation for Eucalyptus plantations based on landsenses ecology

ABSTRACT

Sustainable forest management on a regional scale requires accurate biomass estimation. At present, technologically comprehensive forecasting estimates are generated using process-based ecological models. However, isolation of the ecological factors that cause uncertainty in model behavior is difficult. To solve this problem, this study aimed to construct a meliorization model evaluation framework to explain uncertainty in model behavior with respect to both the mechanisms and algorithms involved in ecological forecasting based on the principle of landsenses ecology. We introduce a complicated ecological driving mechanism to the process-based ecological model using analytical software and algorithms. Subsequently, as a case study, we apply the meliorization model evaluation framework to detect Eucalyptus biomass forest patches at a regional scale (196,158 ha) using the 3PG2 (Physiological Principles in Predicting Growth) model. Our results show that this technique improves the accuracy of ecological simulation for ecological forecasting and prevents new uncertainties from being produced by adding a new driving mechanism to the original model structure. This result was supported by our Eucalyptus biomass simulation using the 3PG2 model, in which ecological factors caused 21.83% and 9.05% uncertainty in model behavior temporal and spatial forecasting, respectively. In conclusion, the systematic meliorization model evaluation framework reported here provides a new method that could be applied to research requiring comprehensive ecological forecasting. Sustainable forest management on regional scales contributes to accurate forest biomass simulation through the principle of landsenses ecology, in which mix-marching data and a meliorization model are combined.