Computer modeling and simulation provide an important tool for understanding and predicting the dynamic behavior of large-scale spatial temporal systems such as wildfire. While sophisticated simulation models have been developed, traditional simulations are largely decoupled from real systems by making little usage of real time data from the systems under study. With recent advances in sensor and network technologies, the availability and fidelity of such real time data have greatly increased. A new paradigm of dynamic data-driven simulation is emerging where a simulation system is continually influenced by the real time data for better analysis and prediction of a system under study. This project investigates tractable approaches for dynamic data driven simulation of large-scale spatial temporal systems based on state of the art probabilistic techniques using Sequential Monte Carlo (SMC) methods. It develops new SMC-based algorithms and computing methods to enhance the effectiveness and efficiency of data driven simulation of large-scale spatial temporal systems. More information please visit my advisor: Dr. Xiaolin Hu's Dynamic Data Driven Simulation for Wildfire Spread Prediction page.
The complexity of wildfire management arises from the uncertain dynamic interactions and dependencies among multiple system components. These include highly dynamic and nonlinear wildfire behaviors, weather conditions, and firefighting resource management. In previous research, these components were largely treated in isolation in their own fields. To achieve effective wildfire management, decision-making support tools that integrate all these components as a whole are needed. This project develops new models and computation methods that integrate weather prediction, wildfire simulation, data assimilation and stochastic optimization for effective wildfire response management. The project includes 1) coupled weather and wildfire modeling and data assimilation for two-way interactive dynamic weather-wildfire prediction, and 2) Integrated wildfire simulation and stochastic optimization for wildfire containment. It also involves parallel/distributed computational methods for fast and robust weather and wildfire behavior predictions.
A wildfire modeling and simulation environment. It supports both wildfire spread simulation and wildfire suppression simulation. A snapshot of the software interface is given below:
Some movies about DEVS-FIRE software, you can find in here
- F. Bai, S. Guo, X. Hu, Towards parameter estimation in wildfire spread simulation based on Sequential Monte Carlo Methods, Proc. 44th Annual Simulation Symposium (ANSS), 2011[PDF]
- S. Guo,F. Bai, X. Hu, Simulation Software as a Service and Service-Oriented Simulation Experiment, Proc. The 2011 IEEE International Conference on Information Reuse and Integration (IRI 2011), 2011[PDF]
- Wildfire Spread and Suppression Simulation and Data Assimilation F.Gu,F. Bai,X. Hu
CSc8530 Parallel Algorithms
I.Fixed Buses (March 14th)
II.Paper Review (March 21st)
Topic Selection (Feb 17th)
Bibliography of literature found (Mar 3rd)
Annotated Bibliography of the literature found (March 17th)
Detailed Annotated Bibliography and Classification of the Results (Mar 31st)
Term Project Presentation Slides(April 18th)
Parallel Monte Carlo Methods