Between classical and ideal: enhancing wildland fire prediction using cluster computing

One of the challenges still open to wildland fire simulators is the capacity of working under real-time constrains with the aim of providing fire spread predictions that could be useful in fire mitigation interventions. We propose going one step beyond the classical wildland fire prediction by linking evolutionary optimization strategies to the traditional scheme with the aim of emulating an “ideal” fire propagation model as much as possible. In order to accelerate the fire prediction, this enhanced prediction scheme has been developed in a fashion on a Linux cluster using MPI. Furthermore, a sensitivity analysis has been carried out to determine the input parameters that we can fix to their typical values in order to reduce the search-space involved in the optimization process and, therefore, accelerates the whole prediction strategy. Baker Abdalhaq received the BSc. Computer Science from Princess Sumaya University College, Royal JordanianSocieaty, Amman Jordania in 1993. In 2001 and 2004, he got the MSc and PhD in Computer Science from Universitat Autónoma de Barcelona (UAB), respectively. His main research interest is focused on parallel fire simulation and, in particular, how to take advantage of the computational power provided for massively distributed systems to enhance wildland fire prediction. Ana Cortés received both her first degree and her PhD in Computer Science from the Universitat Autonoma de Barcelona (UAB), Spain, in 1990 and 2000, respectively. She is currently assistant professor of Computer Science at the UAB, where she is a member of the Computer Architecture and Operating Systems Group at the Computer Science Department. Her current research interests concern software support for parallel and distributed computing including algorithms and software tools for the load-balancing of parallel programs. She has also been working on enhancing wildland fire prediction by exploiting parallel/distributed systems. Tomàs Margalef got a BS degree in physics in 1988 from Universitat Autónoma de Barcelona (UAB). In 1990 he obtained the MSc in Computer Science and in 1993 the PhD in Computer Science from UAB. Since 1988 he has been working in several aspects related to parallel and distributed computing. Currently, his research interests focuses on development of high performance applications, automatic performance analysis and dynamic performance tuning. Since 1997 he has been working on exploiting parallel/distributed processing to accelerate and improve the prediction of forest fire propagation. He is an ACM member. Germán Bianchini received the BSc. Computer Science from Universidad Nacional Del Comahue, Argentina, in 2002. In 2004 and 2006, he got the MSc and PhD in Computer Science from Universitat Autónoma de Barcelona (UAB), respectively. His main research interest is focused on parallel fire simulation and, in particular, how to take advantage of the computational power provided for massively distributed systems to enhance wildland fire prediction. Emilio Luque received the Licenciate in physics and PhD degrees from the University Complutense of Madrid (UCM) in 1968 and 1973 respectively. Between 1973 and 1976 he was an associate professor at the UCM. Since 1976 he is a professor of “Computer Architecture and Technology” at the University Autonoma of Barcelona (UAB), where he is leading the Computer Architecture and Operating System (CAOS) Group at the Computer Science Department. Professor Luque has been the Computer Science Department chairman for more than 10 years. He has been invited lecturer/researcher in Universities of USA, Argentina, Brazil, Poland, Ireland, Cuba, Italy, Germany and PR of China. He has published more than 35 papers in technical journals and more than 100 papers at international conferences and his current/major research areas are: computer architecture, interconnection networks, task scheduling in parallel systems, parallel and distributed simulation environments, environment and programming tools for automatic performance tuning in parallel systems, cluster and Grid computing, parallel computing for environmental applications (forest fire simulation, forest monitoring) and distributed video on demand (VoD) systems.