CyberSEES: Type 2: Computing and Visualizing Optimal Policies for Ecosystem Management

Cyber​​SEES：类型 2：计算和可视化生态系统管理的最佳策略

• 批准号: 1331932
• 负责人: Thomas Dietterich
• 金额: $ 120万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1331932&HistoricalAwards=false
• 关键词: CyberSEES; Type; Computing; Visualizing; Optimal

Intellectual MeritMany ecosystems exhibit spatially spreading processes that we would like to manage to either promote or prevent. For example, we seek to prevent the spread of invasive species while promoting the spread of endangered species. For wildland fire, we seek to promote the spread of low-intensity ground fires that prevent the buildup of dangerous fuels while preventing the spread of high-intensity crown fires that destroy endangered species habitat and valuable timber. These management problems can be formulated mathematically as Markov Decision Problems (MDPs) defined over spatial regions. However, because of the spatial nature of these problems, the MDPs are immense and cannot be solved by any existing algorithms. This project develops new MDP solution algorithms for spatial MDPs. These algorithms will work with ecosystem simulators (rather than requiring explicit models) and they will also address the risk of catastrophic outcomes such as species extinction or catastrophic wildfires. To bridge the gap between the computational solution of an MDP and the actual adoption of such solutions by policymakers, this research develops visualization and interaction methods that will allow stakeholders (e.g., policymakers, land owners, timber industry representatives, conservation biologists) to understand and critique both the problem formulations and the resulting solutions. Broader ImpactsThe new methods will be tested on five management problems: (a) Tamarisk spread in river networks, (b) Cheatgrass spread in Western US range lands, (c) Sudden Oak Death spread in California and Oregon, (d) deciding when to let a wildfire burn versus suppressing it, and (e) deciding where to place fuel reduction treatments in the landscape to reduce fire risk. The Tamarisk, Cheatgrass, and Sudden Oak Death problems will be studied in stylized settings where the relevant environmental properties and costs can be varied. The goal of these studies is to understand how the different spatial spreading processes (exhibited by these different invasive species) determine the structure of the optimal management policy. The results will be published in the literature on natural resource economics and discussed with policymakers in these areas. The wildfire problems ("let burn," and "fuel treatment") will be studied in a real landscape-a publicly-owned site in the Deschutes National Forest containing a mix of Ponderosa and Lodgepole pine. A collaborating fire manager with the US Forest Service will recruit a panel of stakeholders to analyze and critique the proposed management policies using the visualization and interaction tools that we will develop.Problems and techniques developed in this project will form the core of the first Summer School in Computational Sustainability, which will be organized by the research team. The results will also be integrated into the OSU Summer Institute in Eco-Informatics (an NSF REU Site) and the OSU Spring Break Course in Monte Carlo AI for junior undergraduates. Four Ph.D. students and one Postdoc will be trained during this project.

许多生态系统表现出空间上的扩展过程，我们想要设法促进或阻止这些过程。例如，我们努力防止入侵物种的扩散，同时促进濒危物种的传播。对于林地火灾，我们寻求促进低强度地面火灾的蔓延，以防止危险燃料的积聚，同时防止破坏濒危物种栖息地和珍贵木材的高强度树冠火灾的蔓延。这些管理问题可以在数学上表述为空间区域上定义的马尔可夫决策问题（mdp）。然而，由于这些问题的空间性质，mdp是巨大的，无法用任何现有的算法来解决。本项目为空间MDP开发新的MDP解算法。这些算法将与生态系统模拟器一起工作（而不是需要明确的模型），它们还将解决物种灭绝或灾难性野火等灾难性后果的风险。为了弥合MDP的计算解决方案与决策者实际采用这些解决方案之间的差距，本研究开发了可视化和互动方法，使利益相关者（例如决策者、土地所有者、木材行业代表、保护生物学家）能够理解和批评问题的表述和最终的解决方案。更广泛的影响新方法将在五个管理问题上进行测试：(a)柽柳在河流网络中的传播，(b)美国西部牧场上的草的传播，(c)加利福尼亚和俄勒冈州橡树猝死病的传播，(d)决定什么时候让野火燃烧，而不是扑灭它，以及(e)决定在景观中放置燃料减少处理以降低火灾风险。柽柳、绿草和橡树猝死问题将在程式化的环境中进行研究，其中相关的环境特性和成本可以变化。这些研究的目的是了解不同的空间扩展过程（由这些不同的入侵物种表现出来）如何决定最优管理政策的结构。研究结果将发表在有关自然资源经济学的文献中，并与这些领域的决策者进行讨论。野火问题（“让燃烧”和“燃料处理”）将在一个真实的景观中进行研究——一个位于德舒特国家森林的公有场地，里面混合着黄松和洛奇波尔松。与美国林务局合作的消防经理将招募一个利益相关者小组，使用我们将开发的可视化和互动工具来分析和批评拟议的管理政策。在这个项目中开发的问题和技术将成为第一个计算可持续发展暑期学校的核心，该暑期学校将由研究团队组织。研究结果还将整合到俄勒冈州立大学生态信息学暑期研究所（NSF REU网站）和俄勒冈州立大学蒙特卡洛人工智能春假课程中。本项目将培养博士研究生4名，博士后1名。