Modelling Forest Fire Initial Attack Airtanker Operations

The Ontario Ministry of Natural Resources uses airtankers for forest fire suppression that now have onboard GPS units that track their real-time location, velocity and altitude. However, the GPS data does not indicate which fire is being fought, the time each airtanker spends travelling to and from each fire or the time each airtanker spends flying between each fire and the lake from which it scoops water to drop on the fire. A pattern recognition algorithm was developed and used to determine what was happening at each point along the airtanker’s track, including the time and location of every water pickup. This pre-processed data was used to develop detailed models of the airtanker service process. A discrete-event simulation model of the initial attack airtanker system was also developed and used to show how service process models can be incorporated in other models to help solve complex airtanker management decision-making problems.

[1]  Warren E. Walker,et al.  Fire Department Deployment Analysis: A Public Policy Analysis Case Study-- The RAND Fire Project. , 1981 .

[2]  James H. Bookbinder,et al.  Time-Dependent Queueing Approach to Helicopter Allocation for Forest Fire Initial-Attack , 1979 .

[3]  Warren E. Walker,et al.  Measuring the Travel Characteristics of New York City’s Fire Companies , 1974 .

[4]  A. J. Simard A computer simulation model of forest fire suppression with air tankers , 1979 .

[5]  Armann Ingolfsson,et al.  Empirical Analysis of Ambulance Travel Times: The Case of Calgary Emergency Medical Services , 2010, Manag. Sci..

[6]  E. Airtanker Flight Distance between Fires : Random Fire Starts on Line Segments , 2011 .

[7]  Dharma P. Agrawal,et al.  GPS: Location-Tracking Technology , 2002, Computer.

[8]  Peter Kolesar,et al.  Square Root Laws for Fire Engine Response Distances , 1973 .

[9]  Jan M. Chaiken,et al.  Methods for Allocating Urban Emergency Units: A Survey , 1972 .

[10]  M. E. Alexander,et al.  Canadian Forest Fire Danger Rating System: An Overview , 1989 .

[11]  Carl E. Gianino The Rand Fire Project revisited , 1988 .

[12]  David L. Martell,et al.  Performance of initial attack airtanker systems with interacting bases and variable initial attack ranges , 1998 .

[13]  Morton J. M. Posner,et al.  A Time-Dependent Spatial Queueing Model for the Daily Deployment of Airtankers for Forest Fire Control , 2009, INFOR Inf. Syst. Oper. Res..

[14]  Kazi Mohammed Saiful Islam Spatial dynamic queueing models for the daily deployment of airtankers for forest fire control , 1998 .

[15]  M. J. Hodgson,et al.  Location-allocation models for one-strike initial attack of forest fires by airtankers , 1978 .

[16]  Peter Kolesar,et al.  Square-Root Laws for Fire Company Travel Distances , 1975 .

[17]  Robert G. Haight,et al.  Deploying Wildland Fire Suppression Resources with a Scenario-Based Standard Response Model , 2007, INFOR Inf. Syst. Oper. Res..

[18]  David L. Martell,et al.  Basing Airtankers for Forest Fire Control in Ontario , 1996, Oper. Res..

[19]  Kian Aladdini,et al.  EMS Response Time Models: A Case Study and Analysis for the Region of Waterloo , 2010 .

[20]  A. Mason Emergency Vehicle Trip Analysis using GPS AVL Data: A Dynamic Program for Map Matching , 2005 .

[21]  Ron Kohavi,et al.  Emerging trends in business analytics , 2002, CACM.

[22]  Xiaolin Hu,et al.  Integrated simulation and optimization for wildfire containment , 2009, TOMC.

[23]  C. E. Van Wagner,et al.  Development and structure of the Canadian Forest Fire Weather Index System , 1987 .

[24]  Erhan Erkut,et al.  Technical Note - Approximating Vehicle Dispatch Probabilities for Emergency Service Systems with Location-Specific Service Times and Multiple Units per Location , 2009, Oper. Res..

[25]  Reinaldo Morabito,et al.  Analysis of ambulance decentralization in an urban emergency medical service using the hypercube queueing model , 2007, Comput. Oper. Res..

[26]  E. Modeling AirtankerFlight Distance between Concurrent Fires : The Development and Use of Statistical Distribution Parameters , 2011 .

[27]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[28]  R. Sinnott Virtues of the Haversine , 1984 .

[29]  D. Kendall Stochastic Processes Occurring in the Theory of Queues and their Analysis by the Method of the Imbedded Markov Chain , 1953 .

[30]  Edward Ignall,et al.  An Algorithm for the Initial Dispatch of Fire Companies , 1982 .

[31]  J. Goldberg Operations Research Models for the Deployment of Emergency Services Vehicles , 2004 .

[32]  David L. Martell,et al.  An Evaluation of Forest Fire Initial Attack Resources , 1984 .

[33]  Jan M. Chaiken,et al.  Response Areas for Two Emergency Units , 1972, Oper. Res..

[34]  M. Posner,et al.  Single-Server Queues with Service Time Dependent on Waiting Time , 1973, Oper. Res..

[35]  Peter J. Kolesar,et al.  Determining the Relation between Fire Engine Travel Times and Travel Distances in New York City , 1975, Oper. Res..

[36]  Peter Kolesar A Model for Predicting Average Fire Engine Travel Times , 1975, Oper. Res..

[37]  Arthur J. Swersey,et al.  Improving the Deployment of New York City Fire Companies , 1975 .

[38]  Kelvin G. Hirsch,et al.  An overview of LEOPARDS: The Level of Protection Analysis System , 1999 .

[39]  Edward Ignall,et al.  Using Simulation To Develop and Validate Analytical Emergency Service Deployment Models , 1975 .

[40]  F. Massey The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[41]  Erhan Erkut,et al.  Simulation of single start station for Edmonton EMS , 2003, J. Oper. Res. Soc..

[42]  Edward Ignall,et al.  Simulation Model of Fire Department Operations , 1974 .

[43]  Grace M. Carter,et al.  A Simulation Model of Fire Department Operations: Design and Preliminary Results , 1970, IEEE Trans. Syst. Sci. Cybern..

[44]  Peter Kolesar Mathematical programming applications in the analysis of the deployment and utilization of fire-fighting resources , 1973, SMAP.

[45]  S I Harewood,et al.  Emergency ambulance deployment in Barbados: a multi-objective approach , 2002, J. Oper. Res. Soc..

[46]  Claude Dennis Pegden,et al.  Simio: A new simulation system based on intelligent objects , 2007, 2007 Winter Simulation Conference.