Application of Fuzzy Logic for Enhanced Situational Awareness of Surface Wildfires

Application of Fuzzy Logic for Enhanced Situational Awareness of Surface Wildfires

Bibliographic Details
Main Author: Agarwal, Jutshi
Language:English
Published: University of Cincinnati / OhioLINK 2018
Subjects:
Forestry
Forest fire
wildfire
Fuzzy-logic
surface fires
Predictive systems
FBFM
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511856508378518
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ucin15118565083785182021-08-03T07:04:45Z Application of Fuzzy Logic for Enhanced Situational Awareness of Surface Wildfires Agarwal, Jutshi Forestry Forest fire wildfire Fuzzy-logic surface fires Predictive systems FBFM Forest fires are an elemental part of ecosystem management. Not only are they characteristic to evolution, but controlled fires are rudimental in avoiding large wildland fires. Damage to life and property due to uncontrollable wildfires is, however, ineludible. Forest fires, in reality, are a dynamic environment which is constantly responsible for the rapid changes of its factors and in turn, changing its own behavior as an affect. Over the years, forest resource management has focused, largely, on fire suppression and firefighting. This approach to a limited focused technological development has proved perilous to on-field personnel. Inadequate understanding of real-time fire behavior and lack of information on predictable weather conditions put fire-managers in a dangerous situation in an already volatile environment. SIERRA, a student project at the University of Cincinnati, is a step forward in alleviating this problem. With an objective of providing enhanced situational awareness and resource allocation capabilities, it attempts to equip incident commanders with better strategizing techniques. This research is one of the several parts of the larger UAS for SIERRA. The precise factors of real-time fire behavior prediction are converged upon. One of the most crucial step in forest fire spread models is characterizing the subjected vegetation into known fire behavior fuel models. Past methods have been studied and their shortcomings comprehended. Application of fuzzy logic to this otherwise arduous problem has been advocated. A sample Inference System is built for the study of area of West Virginia. The fuel map developed is then applied to a well-established mathematical model for surface fires. Expected fire behavior is mapped based on topographical data developed in ARCGIS. Further, “a worst-case scenario” locus is presented for a range of inclement wind-conditions. The benefits of such an instantaneous system with no to very little training required for transition has been discussed and further scope of the research suggested. 2018-09-04 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511856508378518 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511856508378518 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Forestry
Forest fire
wildfire
Fuzzy-logic
surface fires
Predictive systems
FBFM
spellingShingle Forestry
Forest fire
wildfire
Fuzzy-logic
surface fires
Predictive systems
FBFM
Agarwal, Jutshi
Application of Fuzzy Logic for Enhanced Situational Awareness of Surface Wildfires
author Agarwal, Jutshi
author_facet Agarwal, Jutshi
author_sort Agarwal, Jutshi
title Application of Fuzzy Logic for Enhanced Situational Awareness of Surface Wildfires
title_short Application of Fuzzy Logic for Enhanced Situational Awareness of Surface Wildfires
title_full Application of Fuzzy Logic for Enhanced Situational Awareness of Surface Wildfires
title_fullStr Application of Fuzzy Logic for Enhanced Situational Awareness of Surface Wildfires
title_full_unstemmed Application of Fuzzy Logic for Enhanced Situational Awareness of Surface Wildfires
title_sort application of fuzzy logic for enhanced situational awareness of surface wildfires
publisher University of Cincinnati / OhioLINK
publishDate 2018
url http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511856508378518
work_keys_str_mv AT agarwaljutshi applicationoffuzzylogicforenhancedsituationalawarenessofsurfacewildfires
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