Modeling forest fires risk using spatial decision tree

Forest fires have long been annual events in many parts of Sumatra Indonesia during the dry season. Riau Province is one of the regions in Sumatra where forest fires seriously occur every year mostly because of human factors both on purposes and accidently. Forest fire models have been developed for...

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Main Authors:	Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin, Sitanggang, Imas Sukaesih
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2011
Online Access:	http://psasir.upm.edu.my/id/eprint/47812/ http://psasir.upm.edu.my/id/eprint/47812/ http://psasir.upm.edu.my/id/eprint/47812/1/Modeling%20forest%20fires%20risk%20using%20spatial%20decision%20tree.pdf

id	upm-47812
recordtype	eprints
spelling	upm-478122016-07-15T09:16:44Z http://psasir.upm.edu.my/id/eprint/47812/ Modeling forest fires risk using spatial decision tree Yaakob, Razali Mustapha, Norwati Nuruddin, Ahmad Ainuddin Sitanggang, Imas Sukaesih Forest fires have long been annual events in many parts of Sumatra Indonesia during the dry season. Riau Province is one of the regions in Sumatra where forest fires seriously occur every year mostly because of human factors both on purposes and accidently. Forest fire models have been developed for certain area using the weightage and criterion of variables that involve the subjective and qualitative judging for variables. Determining the weights for each criterion is based on expert knowledge or the previous experienced of the developers that may result too subjective models. In addition, criteria evaluation and weighting method are most applied to evaluate the small problem containing few criteria. This paper presents our initial work in developing a spatial decision tree using the spatial ID3 algorithm and Spatial Join Index applied in the SCART (Spatial Classification and Regression Trees) algorithm. The algorithm is applied on historic forest fires data for a district in Riau namely Rokan Hilir to develop a model for forest fires risk. The modeling forest fire risk includes variables related to physical as well as social and economic. The result is a spatial decision tree containing 138 leaves with distance to nearest river as the first test attribute. IEEE 2011 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/47812/1/Modeling%20forest%20fires%20risk%20using%20spatial%20decision%20tree.pdf Yaakob, Razali and Mustapha, Norwati and Nuruddin, Ahmad Ainuddin and Sitanggang, Imas Sukaesih (2011) Modeling forest fires risk using spatial decision tree. In: 2011 3rd Conference on Data Mining and Optimization (DMO 2011), 28-29 June 2011, Putrajaya, Malaysia. (pp. 103-107). 10.1109/DMO.2011.5976512
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Putra Malaysia
building	UPM Institutional Repository
collection	Online Access
language	English
description	Forest fires have long been annual events in many parts of Sumatra Indonesia during the dry season. Riau Province is one of the regions in Sumatra where forest fires seriously occur every year mostly because of human factors both on purposes and accidently. Forest fire models have been developed for certain area using the weightage and criterion of variables that involve the subjective and qualitative judging for variables. Determining the weights for each criterion is based on expert knowledge or the previous experienced of the developers that may result too subjective models. In addition, criteria evaluation and weighting method are most applied to evaluate the small problem containing few criteria. This paper presents our initial work in developing a spatial decision tree using the spatial ID3 algorithm and Spatial Join Index applied in the SCART (Spatial Classification and Regression Trees) algorithm. The algorithm is applied on historic forest fires data for a district in Riau namely Rokan Hilir to develop a model for forest fires risk. The modeling forest fire risk includes variables related to physical as well as social and economic. The result is a spatial decision tree containing 138 leaves with distance to nearest river as the first test attribute.
format	Conference or Workshop Item
author	Yaakob, Razali Mustapha, Norwati Nuruddin, Ahmad Ainuddin Sitanggang, Imas Sukaesih
spellingShingle	Yaakob, Razali Mustapha, Norwati Nuruddin, Ahmad Ainuddin Sitanggang, Imas Sukaesih Modeling forest fires risk using spatial decision tree
author_facet	Yaakob, Razali Mustapha, Norwati Nuruddin, Ahmad Ainuddin Sitanggang, Imas Sukaesih
author_sort	Yaakob, Razali
title	Modeling forest fires risk using spatial decision tree
title_short	Modeling forest fires risk using spatial decision tree
title_full	Modeling forest fires risk using spatial decision tree
title_fullStr	Modeling forest fires risk using spatial decision tree
title_full_unstemmed	Modeling forest fires risk using spatial decision tree
title_sort	modeling forest fires risk using spatial decision tree
publisher	IEEE
publishDate	2011
url	http://psasir.upm.edu.my/id/eprint/47812/ http://psasir.upm.edu.my/id/eprint/47812/ http://psasir.upm.edu.my/id/eprint/47812/1/Modeling%20forest%20fires%20risk%20using%20spatial%20decision%20tree.pdf
first_indexed	2018-09-07T17:39:21Z
last_indexed	2018-09-07T17:39:21Z
_version_	1610971309198540800

Modeling forest fires risk using spatial decision tree

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