A framework of integrating GIS and parallel computing for spatial control problems - a case study of wildfire control

Complex spatial control problems can be computationally intensive. Timely response in urgent spatial control situations such as wildfire control poses great challenges for the efficient solving of spatial control problems. Web-based and service-oriented architectures of integrating geographic information system (GIS) clients and parallel computing resources have been suggested as an effective paradigm to solve computationally intensive spatial problems. Such real-time coupling framework is highly dependent upon interactivity and on-demand availability of dedicated parallel computing resources appropriate for the problem. We present an approach to enhancing the efficiency of solving spatial control problems while offering another coupling framework of integrating computing resources from desktop GIS and parallel computing environments to alleviate such dependency. Specifically, a model knowledge database is developed to bridge the gap between desktop GIS models and parallel computing resources. Desktop GIS models can iteratively improve themselves by steering rules retrieved from the model knowledge database. To examine its effectiveness, we applied the framework to a wildfire control case. Simulation results show dramatic reduction in computation time of the improved desktop GIS model, and indicate that desktop GIS models enhanced by model knowledge databases can be useful in providing timely assistance on computationally intensive spatial control problems.