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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.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Artificial neural networks to detect forest fire prone areas in the Southeast Fire District of Mississippi./
作者:	Tiruveedhula, Mohan P.
面頁冊數:	100 p.
附註:	Adviser: William H. Cooke, III.
Contained By:	Masters Abstracts International46-06.
標題:	Artificial Intelligence. -
電子資源:	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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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.
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