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

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

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.