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Artificial neural networks to detect...

Mississippi State University., Geosciences.

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Artificial neural networks to detect forest fire prone areas in the Southeast Fire District of Mississippi.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Artificial neural networks to detect forest fire prone areas in the Southeast Fire District of Mississippi./
Author:	Tiruveedhula, Mohan P.
Description:	100 p.
Notes:	Adviser: William H. Cooke, III.
Contained By:	Masters Abstracts International46-06.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1453361
ISBN:	9780549557340

Artificial neural networks to detect forest fire prone areas in the Southeast Fire District of Mississippi.
Tiruveedhula, Mohan P.

Artificial neural networks to detect forest fire prone areas in the Southeast Fire District of Mississippi. - 100 p.

Adviser: William H. Cooke, III.

Thesis (M.S.)--Mississippi State University, 2008.

An analysis of the fire occurrences parameters is essential to save human lives, property, timber resources and conservation of biodiversity. Data conversion formats such as raster to ASCII facilitate the integration of various GIS software's in the context of RS and GIS modeling. This research explores fire occurrences in relation to human interaction, fuel density interaction, euclidean distance from the perennial streams and slope using artificial neural networks. The human interaction (ignition source) and density of fuels is assessed by Newton's Gravitational theory. Euclidean distance to perennial streams and slope that do posses a significant role were derived using GIS tools.

ISBN: 9780549557340Subjects--Topical Terms:

769149
Artificial Intelligence.

Artificial neural networks to detect forest fire prone areas in the Southeast Fire District of Mississippi.
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020 $a 9780549557340
035 $a (UMI)AAI1453361
035 $a AAI1453361
040 $a UMI $c UMI
100 1 $a Tiruveedhula, Mohan P. $3 1018557
245 1 0 $a Artificial neural networks to detect forest fire prone areas in the Southeast Fire District of Mississippi.
300 $a 100 p.
500 $a Adviser: William H. Cooke, III.
500 $a Source: Masters Abstracts International, Volume: 46-06, page: 3423.
502 $a Thesis (M.S.)--Mississippi State University, 2008.
520 $a An analysis of the fire occurrences parameters is essential to save human lives, property, timber resources and conservation of biodiversity. Data conversion formats such as raster to ASCII facilitate the integration of various GIS software's in the context of RS and GIS modeling. This research explores fire occurrences in relation to human interaction, fuel density interaction, euclidean distance from the perennial streams and slope using artificial neural networks. The human interaction (ignition source) and density of fuels is assessed by Newton's Gravitational theory. Euclidean distance to perennial streams and slope that do posses a significant role were derived using GIS tools.
520 $a All the four non linear predictor variables were modeled using the inductive nature of neural networks. The Self organizing feature map (SOM) utilized for fire size risk classification produced an overall classification accuracy of 62% and an overall kappa coefficient of 0.52 that is moderate (fair) for annual fires.
590 $a School code: 0132.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Geotechnology. $3 1018558
650 4 $a Remote Sensing. $3 1018559
690 $a 0428
690 $a 0799
690 $a 0800
710 2 $a Mississippi State University. $b Geosciences. $3 1018556
773 0 $t Masters Abstracts International $g 46-06.
790 $a 0132
790 1 0 $a Cooke, William H., III, $e advisor
791 $a M.S.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1453361