DDDAS-SMRP: Dynamic Data Driven Integrated Simulation and Stochastic Optimization for Wildland Fire Containment

DDDAS-SMRP：动态数据驱动的野地火灾遏制综合仿真和随机优化

• 批准号: 0540000
• 负责人: Lewis Ntaimo
• 金额: --
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-12-01 至 2008-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540000&HistoricalAwards=false
• 关键词: DDDAS; SMRP; Dynamic; Data; Driven

The purpose of the proposed research is to develop a dynamic data driven real-time decision support system for wildland fire spread prediction and containment that integrates simulation and stochastic optimization. The computational aspects of the decision support system include wildland fire spread simulation using the discrete event system specification (DEVS) approach and decision-making under uncertainty concerning where and when to concentrate fire containment efforts using stochastic programming. The experimental measurement aspects of the application include real-time dynamic weather conditions and sensory feedback data that include the actual fire-front position and the effect of fire suppression efforts on fire propagation. The success of the proposed research will derive from the multidisciplinary team of investigators whose areas of research include stochastic programming, discrete event modeling and simulation, systems software, and wildland fire spread.

建议的研究的目的是开发一个动态数据驱动的实时决策支持系统，结合模拟和随机优化的荒地火灾蔓延预测和遏制。决策支持系统的计算方面，包括荒地火灾蔓延模拟使用离散事件系统规范（DEVS）的方法和决策的不确定性下，在哪里和何时集中火灾遏制工作，使用随机规划。该应用程序的实验测量方面包括实时动态天气条件和感官反馈数据，其中包括实际的火灾前沿位置和灭火工作对火灾传播的影响。拟议研究的成功将来自多学科的研究人员团队，他们的研究领域包括随机规划，离散事件建模和仿真，系统软件和野火蔓延。