NDLI: Optimal fireline generation for wildfire fighting in uncertain and heterogeneous environment

Content Provider	IEEE Xplore Digital Library
Author	HomChaudhuri, B. Kumar, M. Cohen, K.
Copyright Year	2010
Description	Author affiliation: Aerosp. Eng. Dept, Univ. of Cincinnati, Cincinnati, OH, USA (Cohen, K.) \|\| Mech. Eng. Dept, Univ. of Cincinnati, Cincinnati, OH, USA (HomChaudhuri, B.; Kumar, M.)
Abstract	Fire is a natural component of many ecosystems but wildland fires often do pose serious threats to public safety, properties and natural resources. Forest fire acts as a dominant factor in reshaping of terrain and change of the ecosystem of a particular area. The total damage due to wildland fire shows an increasing trend over the past decade. Forest Fire Decision Support Systems (FFDSS) have been developed for the last thirty years all over the world that supplies valuable information on forest fire detection, fire behavior and other aspects of forest fires but lacks in developing intelligent fire suppression strategies. In this paper, an effort has been made to generate intelligent fire suppression strategies with efficient resource allocation using the Genetic Algorithm based optimization tool in a heterogeneous and uncertain scenario. The goal of this research is to perform intelligent resource allocation along with the generation of optimal firelines that minimizes the total burned area due to wildland fire. The solutions generated at each generations of the Genetic Algorithm (GA) are used to build the firelines in a heterogeneous terrain where advanced forest fire propagation model is used to evaluate the fitness values of each generated solutions. The optimal firelines thus obtained through the Simulation-Optimization technique minimizes the total damage due to wildland fire and eliminates the chance of any fire escape i.e., firefront reaching the fireline positions before they are built. Such techniques integrated with the existing FFDSS hold promise in effectively controlling forest fires.
Starting Page	5638
Ending Page	5643
File Size	710569
Page Count	6
File Format	PDF
ISBN	9781424474264
ISSN	07431619
e-ISBN	9781424474271
DOI	10.1109/ACC.2010.5531049
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Fires Decision support systems Ecosystems Resource management Fuels Risk management Genetic algorithms Humans Vegetation mapping Mechanical engineering
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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