A CELLULAR AUTOMATA AND INTELLIGENT AGENTS USE TO MODEL NATURAL DISASTERS WITH DISCRETE SIMULATION Pau Fonseca; Josep Casanovas; Jordi Montero Departament d’Estadística i Investigació Operativa i LCFIB., Campus Nord – B6. Universitat Politècnica de Catalunya, c/ Jordi Girona 1-3, 08034 Barcelona (SPAIN) T.+3493 4016941 F.+3493 4017040 E-mail: {pau, josepk, monty}@fib.upc.es ABSTRACT Some of typical industry problems can be analyzed using discrete simulation, in concrete the simulation event scheduling paradigm has been used over decades in industry area offering good results. However, although simulation can represent the reality closer than any other approximation, in other systems like ecological, economical or social don’t present similar results. In fact, currently is very difficult to depict this kind of systems. One of the first problems resides in his evolving nature. But, event scheduling simulations can be used in this sort of systems adding cellular automata and intelligent agents to the model structures, adding intrinsic evolving nature. Although the prediction usually cannot be done, there are other important benefits that can be considered, like data collection between the researchers of the domain, the recognition of gaps in the knowledge, and of course, better understanding of system in a global approximation. Nowadays these are the main goals attended to construct this sort of models. The brief description of this architecture, which allows the simulation of evolving systems, like natural disasters, is the target of this paper. KEY WORDS Cellular automata, intelligent agents, natural disaster, event scheduling, GIS. 1. Introduction This paper merge three important areas, discrete event simulation, that represent the simulation model kernel and determines main system architecture, GIS data manipulation and his utilization inside a simulation model which enables landscape data use inside simulation model, and finally use of artificial intelligent techniques (like cellular automaton or intelligent agents) in order to enable system evolution. Natural disasters, like wildfire, flooding, earthquakes or tsunamis, currently are unpredictable, and represent one of the biggest hazards to population of some earth areas. Understand through simulation techniques this disasters represent not only the possibility of human life salvation, also enables the capability of calculate the behavior of some other different systems that present evolving behavior. In this paper a wildfire propagation and containment model is presented like a sample. Propagation model is based in the BEHAVE model (Andrews 1986, Andrews and Chase 1989, Burgan and Rothermel 1984, Andrews and Bradshaw 1990) [15] [1]. 2. GIS Data Natural disasters simulation models are focused in nature, and for this reason may require a massive set of GIS data, in our wildfire sample is necessary to define land slope, vegetation, combustible, wind speed, etc, consequently is necessary establish an interface, between this data and simulation model, that enables the representation and his use in the model. Data that represents the elements (layers) are stored in files that follows IDRISI raster format (IDRISI is a GIS developed by Clark university). Each of these files represents a matrix, and each one of their cells defines propagation model features (all layers are represented by two files *.img file that contains data, the matrix, and *.doc files that contains data documentation). The files used are (following the behave model): • Mapa: file containing the DEM (Digital Elevation Model). • Model: file that represents the propagation model implemented for each cell. • Slope, Aspect: files that stores the slope and his direction. These files are calculated using the DEM. (Mapa files) • M1, M10, M100, Mherb, Mwood: files that contains the combustible description. The results files are two: • ignMap.dtm: Stores ignition time. • flMap.dtm: Stores flames elevation. All these data can be represented in virtual reality format, which enables landscape representation. The relevance of this technology is not only in the visual representation facet, also in his computer data representation. Virtual reality stores physical structures of landscape, structures that are required by the simulator in order to enable the interaction with the landscape elements. This also enables