防洪物资储备决策方法初探

根据防洪抢险物资储备属于一种信息不全型决策问题的特点,本文提出了一种用于确定每年防灾物资准备量概率排序决策模型,并以孙不河流泥石流灾害预报火灾决策为例进行说明。     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.     

An Objective Multiattribute Analysis Approach for Allocation of Scarce Irrigation Water Resources1

Zardari, Noor ul Hassan, Ian Cordery, and Ashish Sharma, 2010. An Objective Multiattribute Analysis Approach for Allocation of Scarce Irrigation Water Resources. Journal of the American Water Resources Association (JAWRA) 46(2):412-428. DOI: 10.1111/j.1752-1688.2009.00410.x Abstract: In this study, end user (farmer) and decision maker (water allocator) opinions were surveyed and a conjoint analysis (CA) based method was applied to the quantitative and qualitative data to assess the utility associated with each attribute that plays a role in forming the final thinking of the water users. The application of CA for estimating the utility for each attribute level is a novel approach, which helps provide a formal, objective basis for assigning relative scales for each attribute interval within a multiattribute decision-making model. The utilities (part-worths) obtained from the CA have a cardinal scale and were found to be comparable within and across the attributes. A farmers’ survey on five water allocation attributes was completed from 62 farmers and their opinions on the relative importance of attributes were elicited for a subarea of the Indus River Basin. The CA method was then applied to the survey data and the utilities for each attribute level were determined. This allowed, for instance, decisions to be made, which take account of the perceived value of the water and of the availability of local labor to work on the farm. Finally, these interval scales were used within the specification of the ELECTRE multiattribute decision-making method to provide a complete and objective ranking of nine irrigation districts so that the best decisions on water allocation could be made.     

Setting the most robust effluent level under severe uncertainty: Application of information-gap decision theory to chemical management

Decisions in ecological risk management for chemical substances must be made based on incomplete information due to uncertainties. To protect the ecosystems from the adverse effect of chemicals, a precautionary approach is often taken. The precautionary approach, which is based on conservative assumptions about the risks of chemical substances, can be applied selecting management models and data. This approach can lead to an adequate margin of safety for ecosystems by reducing exposure to harmful substances, either by reducing the use of target chemicals or putting in place strict water quality criteria. However, the reduction of chemical use or effluent concentrations typically entails a financial burden. The cost effectiveness of the precautionary approach may be small. Hence, we need to develop a formulaic methodology in chemical risk management that can sufficiently protect ecosystems in a cost-effective way, even when we do not have sufficient information for chemical management. Information-gap decision theory can provide the formulaic methodology. Information-gap decision theory determines which action is the most robust to uncertainty by guaranteeing an acceptable outcome under the largest degree of uncertainty without requiring information about the extent of parameter uncertainty at the outset. In this paper, we illustrate the application of information-gap decision theory to derive a framework for setting effluent limits of pollutants for point sources under uncertainty. Our application incorporates a cost for reduction in pollutant emission and a cost to wildlife species affected by the pollutant. Our framework enables us to settle upon actions to deal with severe uncertainty in ecological risk management of chemicals.     

水污染突发事件：演化模型与应急管理

概述了突发事件演化研究的现状,总结了现阶段中国水污染突发事件的典型案例,研究了事件的演化过程,提出把社会因素和全过程干预作为水污染突发事件演化动因的一个重要方面,并在此基础上分析了水污染突发事件演化的主要动力因素,运用灾害学、应急管理、环境科学等相关理论构建了事件演化的动力因素体系,最后从系统分析的角度构建了水污染突发事件演化模型。研究表明：社会因素和应急干预对水污染突发事件的演化具有推动作用;事件演化具有阶段性,不同阶段的动力因素存在差异;水污染突发事件应急管理具有多目标性,单一目标应急管理不能有效控制水污染突发事件演化。研究水污染突发事件演化机理可为政府有效地预警与事件初期的应急决策提供重要的理论依据。     

基于静态特征和动态行为的火灾检测模型

为实时准确地检测视频监控的火灾信号,减少火灾误报,提出1种基于静态特征和动态行为的火灾检测方法.利用改进的Faster RCNN检测模型,根据可疑火灾区域的颜色特征和空间特征对其进行目标检测和特征降维,与传统的Faster RCNN相比平均检测精度提升5％;利用ILSTM对连续帧中的特征进行累加,对短期内是否发生过火灾...     

Design and evaluation of a local analytic-deliberative process for climate adaptation planning

In the midst of rapidly proliferating engagement efforts around climate adaptation, attention to the design and evaluation of decision support processes and products is warranted. We report on the development and evaluation of a process framework called the Vulnerability, Consequences, and Adaptation Planning Scenarios (VCAPS) process. VCAPS is a systematic approach to integrate local knowledge with scientific understanding by providing opportunities for facilitated, deliberative learning-based activities with local decision makers about climate change vulnerability and adaptation. We introduce the conceptual basis of the process in analytic-deliberation, hazard management, and vulnerability. Our evaluations from eight coastal communities where the approach was applied point to four assets of VCAPS: it promotes synthesis of local and scientific knowledge; it stimulates systems thinking and learning; it facilitates governance by producing action plans with transparent justifications; and it accommodates participant time constraints and preferences.     

Structuring Decisions for Managing Threatened and Endangered Species in a Changing Climate

The management of endangered species under climate change is a challenging and often controversial task that incorporates input from a variety of different environmental, economic, social, and political interests. Yet many listing and recovery decisions for endangered species unfold on an ad hoc basis without reference to decision‐aiding approaches that can improve the quality of management choices. Unlike many treatments of this issue, which consider endangered species management a science‐based problem, we suggest that a clear decision‐making process is equally necessary. In the face of new threats due to climate change, managers’ choices about endangered species require closely linked analyses and deliberations that identify key objectives and develop measurable attributes, generate and compare management alternatives, estimate expected consequences and key sources of uncertainty, and clarify trade‐offs across different dimensions of value. Several recent cases of endangered species conservation decisions illustrate our proposed decision‐focused approach, including Gulf of Maine Atlantic salmon (Salmo salar) recovery framework development, Cultus Lake sockeye salmon (Oncorhynchus nerka) management, and Upper Columbia River white sturgeon (Acipenser transmontanus) recovery planning. Estructuración de Decisiones para Manejar Especies Amenazadas y en Peligro en un Clima Cambiante     

Use of an Action‐Selection Framework for Human‐Carnivore Conflict in the Bangladesh Sundarbans

Abstract: Human–carnivore conflict is manifested in the death of humans, livestock, and carnivores. The resulting negative local attitudes and retribution killings imperil the future of many endangered carnivores. We tailored existing management tools to create a framework to facilitate the selection of actions to alleviate human–carnivore conflict and applied the framework to the human–tiger conflict in the Bangladesh Sundarbans. We identified potential actions that consider previous management efforts, local knowledge, cost‐effectiveness, fieldwork experience of authors and project staff, previous research on tiger ecology by the authors, and recommendations from human–carnivore conflict studies in other countries. Our framework includes creation of a profile to improve understanding of the nature of the conflict and its underlying causality. Identified actions include deterrents, education, direct tiger management, and response teams. We ranked actions by their potential to reduce conflict and the monetary cost of their implementation. We ranked tiger‐response teams and monitoring problem tigers as the two best actions because both had relatively high impact and cost‐effectiveness. We believe this framework could be used under a wide range of human–wildlife conflict situations because it provides a structured approach to selection of mitigating actions.     

Value of information in product recovery decisions: a Bayesian approach

There is a widespread recognition of the need for better information sharing and provision to improve the viability of end-of-life (EOL) product recovery operations. The emergence of automated data capture and sharing technologies such as RFID, sensors and networked databases has enhanced the ability to make product information; available to recoverers, which will help them make better decisions regarding the choice of recovery option for EOL products. However, these technologies come with a cost attached to it, and hence the question ‘what is its value?’ is critical. This paper presents a probabilistic approach to model product recovery decisions and extends the concept of Bayes' factor for quantifying the impact of product information on the effectiveness of these decisions. Further, we provide a quantitative examination of the factors that influence the value of product information, this value depends on three factors: (i) penalties for Type I and Type II errors of judgement regarding product quality; (ii) prevalent uncertainty regarding product quality and (iii) the strength of the information to support/contradict the belief. Furthermore, we show that information is not valuable under all circumstances and derive conditions for achieving a positive value of information.