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.

拟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，用于野生火灾传播预测和遏制，以整合仿真和随机优化。决策支持系统的计算方面包括使用离散事件系统规范（DEVS）方法（DEV）方法和决策在何时何地使用随机编程集中消防遏制工作的不确定性，使用随机编程。该应用程序的实验测量方面包括实时动态天气条件和感觉反馈数据，其中包括实际的火 - 前位置以及抑制火灾对火灾传播的影响。拟议研究的成功将源自研究人员的多学科团队，其研究领域包括随机编程，离散事件建模和仿真，系统软件和Wildland Fire蔓延。