PERUN a system for early warning and simulation of forest fires and other natural or human-caused disasters

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1 PERUN a system for early warning and simulation of forest fires and other natural or human-caused disasters N. Dobrinkova Institute for Parallel Processing, Bulgarian Academy of Sciences, bl. 25A, Acad. G. Bonchev Str, Sofia 1113, Bulgaria L. Nedelchev Central Laboratory of Optical Storage and Processing of Information, Bulgarian Academy of Sciences, bl. 101, Acad. G. Bonchev Str, Sofia 1113, Bulgaria Abstract. Wildland fires are dangerous phenomena that wipe out vast areas with forests every year and can cause loss of life. The final result is miles of burned area, some of which protected zones with rare species of the flora and fauna. This paper describes a methodology for constructing a system which aims to support the human expert s decision making in fire control. 1. Introduction The study of wildland fire should begin with the basic principles and mechanisms of the combustion process fire fundamentals. Fire behavior is what a fire does, the dynamics of the fire event. An understanding of the fundamentals of wildland fire is important for some very practical reasons. The combustion process can be manipulated to some extent: Retardants can be applied to affect the combustion process: fuel arrangement can be altered for hazard reduction; and appropriate environmental conditions can be chosen for prescribed fire to reduce smoke impacts, achieve desired fuel reduction, and still retain control of the fire. The need to understand wildland fire fundamentals is even more pressing than it was in the past. In earlier times the focus was on describing the aspects of fire that are important to suppression efforts. That continues to be high priority. In addition, there is now increasing emphasis on characterizing fire for its effect on vegetation and for the smoke it produces[1]. The basis for the fire modeling is the Rothermel model for the behaviour of surface fires [2]. It calculates for any given point local intensity and spread parameters for the head of a surface fire. Inputs for the model are a two-dimensional wind field, terrain parameters, fuel moisture and a detailed description of the fuel bed. Based on the local behaviour output by the Rothermel model and on a model for the local shape of fire spread [3], the spread from a set of source locations can be simulated. The influence of barriers (streets, rivers, fuel breaks, etc.) is addressed with a probabilistic model based on the width of the barrier and the flame length. The spread simulation also allows the calculation of the flame length on the entire fire perimeter, which in turn is an important factor for the success of various types of fire suppression activities [4]. The mathematical models require descriptions of fuel properties as inputs to calculations of fire danger indices or fire behavior potential. The set of parameters describing the fuel characteristics have become known as fuel models and can be organized into four groups: grass, shrub, timber, and slash. Fuel models for fire danger rating have increased to twenty while fire behavior predictions and applications have utilized the thirteen fuel models tabulated by Rothermel [2] and Albini [5]. Each fuel model is described by the fuel load and the ratio of surface area to volume for each size class; the depth of the fuel bed involved in the fire front; and fuel moisture, including that at which fire will not spread, called the moisture of extinction. The fire models used nowadays by wildland fire managers are specified to distinct types of fire behavior. There are separate models for surface fires [2,5], crown fires, which have been theoretically investigated in great details by Perminov [6-9], spotting [10,11], and point-source fire acceleration [12,13]. These behaviors are really abstractions of an overall three-dimensional process of unconfined combustion that links implicitly to the environment through heat and mass transfer feedbacks. Not only fire behavior models were developed during the years of examinations in the field. The way of predicting the fire behavior has been a transition from nomograms to calculator, and than to computer based systems. For example in the US this transition started with the hand-held TI-59 calculator of Texas Instruments with a Fire Danger/Fire Behavior Custom Read Only Memory (CROM). The calculator method has been a project initiated by John Deeming, Jack Cohen, and Bob Burgan, and finished by Bob Burgan. It covered the needs, of the fire managers at that time from dispatchers to 1

2 regional planners [14-16]. The quantitative descriptors of fire behavior have become more widely used due to the prevalence of automated systems [17]. Than PC-based programs for fire behavior has been developed starting with FSPro fire spread probabilities; FARSITE fire area simulator; FlamMap fire mapping and analysis system and BehavePlus fire modeling system. The last one is using the fire algorithms from the previous three systems and is a collection of models that describe the fire and its environment. Fire behavior systems have been developped not only in the US, but also in Australia, Canada and Europe. Currently, a few EDSSs (Environmental Decision Support Systems) have been constructed to support fire managers in their decision making [18-20]. The CHARADE project initiated by Ricci et al. [21] is a software platform for the development of intelligent EDSSs and has been used to construct a working EDSS for managing first intervention in forest fires. The planning system integrates case-based reasoning and constraint reasoning as used by Avesani et al. and is integrated with a Geographic Information System (GIS) for displaying spatial data and a model for simulating forest fires. Kourtz describes various applications of expert systems and AI (artificial intelligence) algorithms to support managing Canadian forest fires [20]. Example applications are improving fire detection, computing cost effective resource allocation and predicting forest fire occurrence. Based on this experience a team from Bulgarian Academy of Sciences (BAS), together with a European consortium came up to the idea about developing a software fire behavior platform. At first the program will be adapted for the Bulgarian fuel and meteorological specifics, but later on shall be expanded and integrated with similar systems in the neighboring countries or all over Europe. 2. Forest fire prevention in Bulgaria until present 2.1. Statistics for Southern Europe and Bulgaria Forest fires are problem in most of the south European countries, because of the dry climate during the summer and the year-round high temperatures. Statistics have been done among the south European EU member states and the results are presented in Figure 1 below. Number of Fires Year Figure 1. The number of fires in the five Southern EU-Member States for the last 26 years (Bulgaria and Romania are still associate in 2006) Although Bulgaria and Romania haven t been included in EU statistics to the end 2006, Bulgaria has done statistic about forest fires in the period There are also data about the number of forest fires every year collected between [22]. This information is published by the National Fire Safety and Civil Protection Service, part of the Bulgarian Ministry of Interior. 2

3 Брой пожари Година (a) (b) Figure 2. (a) Bulgarian forest fires in the period , separated by regions; (b) total number of the forest fires per year in Bulgaria between 1971 and 2006 It is clearly seen from Fig. 2(b) that there is a considerable increase of the number of forest fires in Bulgaria after 1990 and especially in 1993 and 2000 when fire activity peaked and more than 1000 wildland fires devastated huge areas of forests in the lowlands as well as in the mountains. The number of fires and the average size of the fires have increased about 6 times in the recent years, however the total burned area and the percentage of vegetation burned during a year has increased dramatically - more than 30 times [23]. Even though the number of wildfires is increasing and the consequences are not only of environmental, but also of economical and social significance, proper solution haven t been found. At present most of the countries suffering from wildfires are dealing with the disaster in the moment of its occurrence. It would be much easier to avoid the long and very expensive period of handling with the fire if a working system for early warning and prediction is observing the areas with high risk of fires Aero-Spatial Observation Center (ASOC) The center was created in Sofia on 7 th of July 2007 to provide the disaster control units with adequate, real time information and to ensure better coordination and effectiveness in the prevention of natural hazards. It is the first aerospace center in Bulgaria. It s aimed to improve and streamline the process of early warning, prediction and monitoring of natural disasters and accidents on national scale. It allows discovering and following the dynamics of wildland and forest fires, floods, to estimate the loss of forest, control the conditions of the vegetation, soil humidity and erosion as well as air pollution. There are four main sources of information received by the center: Satellite images from the Dartcom system. Information is received on every 40 min with resolution of 1 km and in five frequency ranges. Images from MODIS system once per day. Images from the DMC (Disaster Monitoring Constellation) with much higher resolution, namely 32m, received once on every two or three days depending on the satellites trajectory. System for aerospace monitoring via a radio navigated or human navigated airplane. Data are sent directly to the center by satellite connection. The system operates on national level and is also used by other governmental organizations e.g. Ministry of Economy and Energy, Ministry of Environment and Water Supplies, Ministry of Agriculture and Forestry and others. The center is in 24/7 operation. However ASOC still lacks a system for automated satellite images recognition and early detection of forest fires, floods and other natural or human-caused disasters. Introducing such system will considerably increase the effectiveness and usefulness of the information gathered and will gradually transfer this center into a national (or even international) emergency center. 3. The PERUN system a glance into the future The aim of the project PERUN (Prediction of the consequences in case of natural or human-caused disasters, simulation system for early warning) is to integrate environmental monitoring and management to assess population exposure and health risks and to organize efficient response. The project presents development of an ICT system, connected with global GPS/GIS, for processing information obtained from earth surface monitoring and to detect and reveal appearance of forest fires. The system would allow predicting the evolution of the fires by their shape as well as by the dynamical processes i.e. the speed of spreading depending on the variables of the area relief and the meteorological conditions at the given moment. In the case of forest fires, besides the direct damages, there are also emissions formed in the process of burning which contribute to the air pollution in general and affect the climate on a large scale not only in Bulgaria. Predicting fire behavior and the expected impact on the environment allows taking adequate and timely measures to restrict the negative consequences and to warn the neighboring countries for the expected harmful influences. It is foreseen to create a distributed network of local stations exchanging information between each other via the global information system (Internet), which would allow expanding the system in the time and cover as many countries as possible. 3

4 3.1. Steps in forest fire control Forest fires depend on three things: fuel, heat and oxygen. Taking away one of them will put out the fire. Methods (resources) for controlling the fire can be divided into airborne agents and ground agents. Airborne agents such as airplanes and helicopters drop water or chemical retardants in front of the fire and take away heat or oxygen. Ground agents such as trucks or land rovers are equipped with water tanks for directly attacking the fire. Other ground agents are bulldozers, tractors or people equipped with e.g. chain saws. These agents cut fire lines, which is an effective means for removing fuel. The choice of methods and equipment which are actually used depends on the country and kind of environment [24]. The system is based on the initial environmental state at the moment of fire occurrence. Than the system determines how the environmental state will change during each time step as modeling an animated output The environmental model The environment is represented as a grid-based model. We have for each cell of the earth surface; static and dynamic variables. There are many variables which we need to considered for modelling the spread of fire, but we first consider only the most important ones: wind speed and wind direction. When the wind is strong, the fire spreads much faster and we have to take this into account. The wind direction influences the direction in which the fire is propagating. The static variables describe the state of each grid cell by the following attributes: 1) Terrain with the possible set of states: {trees, grass, empty, water, asset}. Here assets are special states which have to be protected at high cost. 2) Fire activation, a continuous variable which denotes how much the fire intensity is at a particular grid cell. It determines when a grid cell starts burning and how much heat it transfers to neighboring cells. The project focuses on building up a Local Emergency Centers (LECs), which will collect all the variables above as input information. The general structure of LEC is given on figure 1. GPS HMS HMS HMS R e c o g n itio n m o d e l Monitor Dynamical processes m o d e l Static database Data processing secure port external influences assesm ent 2D/3D visualisation lo c a l g o v e rn m e n t civil protection fire departm ents other LECs Figure 3. General structure of a Local Emergency Center (LEC) The Monitor is the heart of the system. It computes on the base of the fire spread model, along with the image recognition model the first stage of the fire behavior. The Hydro Meteorological Stations (HMS) provides the information about the weather conditions for the specific grid cell. Each country has such stations and that information is always available. Wireless connections between LECs and HMS are important to be established, because of the faster data exchange. The static database is a database with all the variables describing each grid cell, which don t change in the time like relief, slope, water resources etc. 4

5 Once all the variables are collected and computed the output is a 2D/3D image which can be transmitted through a secure port straight to the local civil protection fire departments or other LECs near by in order to assist them when taking the most appropriate measures to prevent the spreading and finally to extinguish the fire Fire spreading and image recognition models. Developing fire spreading and image recognition models is a difficult task. We have to trade-off between accuracy and efficiency of the models. If we want to model the forest fire dynamics very accurately, detailed knowledge about the environment should be given and the computational demands for simulating the fire would easily become overwhelming. On the other hand, it may be argued that if we make it too simple, we risk that our algorithms will not learn to control realistic fires. Therefore, we propose to incrementally increase the complexity of the models. We do not immediately include variables such as temperature, drought, and weather conditions (rainfall) in the Monitor. We also do not use variables such as moisture, slope and slope direction (for mountains), and the height of flames. The simplistic models allow us to study learning fire fighting strategies without getting confused by all the details. After we construct algorithms which work well, we will add different fuel types, forest fire types, weather types etc. Data from field experiments can be used to design, calibrate and extend the models, but the opportunities to burn are limited in Bulgaria. 4. Conclusions Wildland fires are problem not only in Southern Europe and Bulgaria. Due to the global warming the number of fires increases every year. The consequences affect directly the environment and people s life on economic and social levels. Here we present a framework for building a system which can help to control forest fires. This would combine the efforts of scientists, whose main fields of study are sustainable development, environmental problems, information technologies, mathematics etc. The final goal is to create an effective Environmental Decision Support System, which shall streamline the work of the disaster management units and in particular to support fire managers in their decision making. 5. List of References [1] Pyne, S. J., Andrews, P. L., Laven, R. D. (1996) Introduction to Wildland Fire, Wiley, New York, p.3 [2] Rothermel, R. C. (1972) A mathematical model for predicting fire spread in wildland fuels. Research Paper INT-115. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, pp [3] Anderson, H. E. (1983) Predicting Wind-Driven Wild Land Fire Size and Shape. Research Paper INT-305. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, pp [4] Rothermel, R. C. (1983) How to predict the spread and intensity of forest and range fires. General Technical Report INT-143. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, pp [5] Albini, Frank A Estimating wildfire behavior and effects. USDA For. Serv. Gen. Tech. Rep. INT-30, 92 p. lntermt. For. and Range Exp. Stn., Ogden, Utah. [6] Perminov V.: Numerical Solution of Reynolds Equations for Forest Fire Spread. Lecture Notes in Computer Science, Vol (2002) [7] Perminov V.: Mathematical Modeling of Crown Forest Fires Initiation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2667, pp , 2003 [8] Van Wagner, C.E Conditions for the start and spread of crownfire. Can. J. For. Res. 7: [9] Van Wagner, C.E Prediction of crown fire behavior in two stands of jack pine. Can. J. For. Res. 23: [10] Muraszew, A. and J.B. Fedele Statistical model for spot fire hazard. Aerospace Report ATR-77(7588)-1. Engineering Science Operations, The Aerospace Corporation, El Segundo, CA. Report to USDA For. Serv. Riverside Forest Fire Laboratory, PSW Grand No. 22. [11] Albini, F.A Spot fire distance from burning trees - a predictive model. USDA For. Serv. Gen. Tech. Rep. INT-56. [12] Cheney, N.P Fire Behavior. In: Gill, A.M., R.H. Groves, and I.R. Noble (eds.) Fire and the Australian biota. Aust. Acad. Science. Canberra. [13] Cheney, N.P. and J.S. Gould Letter to the editor: fire growth and acceleration. Intl. J. Wildl. Fire 7(1):1-6. [14] Burgan, Robert E.; Cohen, Jack D.; Deeming, John E. Manually calculating fire-danger ratings 1978 National Fire-Danger Rating System. Gen. Tech. Rep INT-40. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station: p. [15] Burgan, Robert E. A handheld calculator fire danger and fire behavior. In: Sixth conference on fire and forest meteorology: proceedings; 1980 April 22-24; Seattle, WA. Washington, DC: Society of American Foresters; 1980: [16] Burgan, Robert E. Fire danger/fire behavior computations with the Texas Instruments TI-59 calculator: user s manual. Gen. Tech. Rep. INT-61. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; p. [17] Rothermel, Richard C. Fire behavior systems for fire management. In: Sixth conference on fire and forest meteorology: proceedings; 1980 April 22-24; Seattle, WA. Washington, DC: Society of American Foresters; 1980:

6 [18] Beer, T. (1990): The Australian national bushfire model project. Mathematical and Computer Modelling, 13, (12): [19] Avesani, P., Perini, A., Ricci, F. (1993): Combining CBR and constraint reasoning in planning forest fire fighting. In Proceedings of the first European workshop on Case- Based reasoning, pages [20] Kourtz, P. (1994) Advanced information systems in Canadian Forest Fire Control, AFAC Conference, Australia. [21] Ricci, F., Mam, S., Marti, P., Normand, P., Olmo, P. (1994): CHARADE: a platform for emergencies management systems. Technical Report , IRST, Trento. [22] Internet resources of the National Fire Safety and Civil Protection Service of Bulgaria [23] Ecopolis, bulletin 48 (2001), Forest fires reach catastrophic scales (In Bulgarian) [24] Calabri, G. (1982): Recent evolution and prospects for the Mediterranean region. In van Nao, T., editor, Forest fire prevention and control: Proceedings of an international seminar. Martinus Nijhoff and Dr W. Junk. 6