Development of forest fire management decision support systems

森林火灾管理决策支持系统开发

• 批准号: RGPIN-2015-04936
• 负责人: Martell, David
• 金额: $ 2.04万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613265
• 关键词: Development; forest; fire; management; decision

I’m requesting NSERC funds to develop and field test 1) a forest fire detection patrol routing model, 2) an initial attack airtanker deployment model and 3) a large fire containment optimization model. My students and I will develop novel models of fire management systems that; 1) will be of interest to operational researchers, fire researchers and managers and 2) can be used to enhance forest fire management in Canada and elsewhere. 1) Forest fire detection patrol routing model: Patrol aircraft are often used to search for forest fires but there are, to my knowledge, no models that are suitable for predicting the probability that airborne detection observers will detect small un-reported fires burning near their flight lines. We will use Global Positioning System (GPS) data that tracks detection aircraft to develop a detection probability model that we will use to develop a daily detection demand model that will be used to determine how important it is for a detection patrol aircraft to visit each location in a protected area each day. We will then use vehicle routing methods to help decide how to satisfy the detection demand each day, and field test our approach in northwestern Ontario. 2) Initial attack airtanker deployment model: Initial attack airtanker systems are complex multi-server spatial queueing systems with fire arrival rates that vary over time and space and service times that depend on waiting time as fires grow while they wait in the queue. My students and I have developed queueing models of airtanker systems but there remain important challenges, two of which are; 1) the development of new service process models and their incorporation in more realistic queueing models of initial attack airtanker systems and 2) the development of daily airtanker deployment optimization models. We will use GPS data provided by the Ontario Ministry of Natural Resources and Forests (OMNRF) to develop a new initial attack airtanker system model. We will then use stochastic optimization methods to develop a decision support system (DSS) that can be used to help determine how best to deploy the airtankers each day, and field test it in northwestern Ontario. 3) Large fire containment optimization model: Large forest fires are managed by Incident Management Teams (IMTs) that often have only 3 to 4 hours to develop a containment strategy that will satisfy the local land manager’s objectives under considerable uncertainty concerning weather and other factors, safely and at a reasonable cost. During the past 5 fires seasons I was assigned as a researcher, to an OMNRF IMT, to study their planning and decision-making problems and I have, in collaboration with others, developed a simple deterministic “proof of concept” large fire containment optimization model. I will incorporate what I have learned about large fire management in a more realistic containment optimization model that I will field test in collaboration with an IMT.

我正在请求NSERC资金来开发和现场测试1)森林火灾探测巡逻路线模型，2)初始攻击空中加油机部署模型和3)大型火灾遏制优化模型。我和我的学生将开发新的火灾管理系统模型，这些模型：1)将引起运筹学研究人员、火灾研究人员和管理人员的兴趣，2)可用于加强加拿大和其他地方的森林火灾管理。 1)森林火灾探测巡逻路线模型：巡逻机经常用于搜索森林火灾，但据我所知，没有任何模型适合预测空中探测观察员在其飞行路线附近发现未报告的小火灾的概率。我们将使用跟踪侦察飞机的全球定位系统(GPS)数据来开发一个侦测概率模型，我们将使用该模型来开发每日侦察需求模型，该模型将用于确定侦察巡逻机每天访问保护区内每个地点的重要性。然后，我们将使用车辆路径方法来帮助确定如何满足每天的检测需求，并在安大略省西北部对我们的方法进行现场测试。 2)初始攻击空中加油机部署模型：初始攻击空中加油机系统是一个复杂的多服务台空间排队系统，其火力到达率随时间、空间和服务时间的变化而变化，而等待时间取决于等待时间。我和我的学生已经开发了空中加油机系统的排队模型，但仍然存在重要的挑战，其中两个是：1)开发新的服务流程模型，并将其纳入更现实的初始攻击空中加油机系统的排队模型；2)开发日常空中加油机部署优化模型。我们将使用安大略省自然资源和林业部(OMNRF)提供的GPS数据来开发一种新的初始攻击空中加油机系统模型。然后，我们将使用随机优化方法开发一个决策支持系统(DSS)，该系统可用于帮助确定如何最好地部署每天的加油机，并在安大略省西北部进行现场测试。 3)大型火灾控制优化模型：大型森林火灾由事故管理小组(IMTS)管理，他们通常只有3到4个小时的时间来制定控制策略，在天气和其他因素存在相当大的不确定性的情况下，以合理的成本安全地满足当地土地管理者的目标。在过去的5个火灾季节中，我被指派为OMNRF IMT的研究员，研究他们的规划和决策问题，我与其他人合作，开发了一个简单的确定性“概念证明”大型火灾遏制优化模型。我将把我所学到的关于大型火灾管理的知识融入到一个更现实的遏制优化模型中，我将与IMT合作进行现场测试。