NDLI: Hotspot occurrences classification using decision tree method: Case study in the Rokan Hilir, Riau Province, Indonesia

Content Provider	IEEE Xplore Digital Library
Author	Sitanggang, I.S. Ismail, M.H.
Copyright Year	2010
Description	Author affiliation: Forest Surveying & Engineering Laboratory, Faculty of Forestry, University Putra Malaysia (Ismail, M.H.) \|\| Computer Science Department, Bogor Agricultural University, Bogor, Indonesia (Sitanggang, I.S.)
Abstract	Application of geospatial and data mining techniques in forest fires research have resulted interesting and useful information in decision making related to the forest fires management. This paper presents a result of the study in applying the C4.5 algorithm on a forest fire dataset in the Rokan Hilir district, Riau Province, Indonesia. The dataset consists of hotspot occurrence locations, human activity factors, and land cover types. Human activity factors include city center locations, roads network and rivers network. The results were a decision tree which contains 18 leaves and 26 nodes with accuracy about 63.17%. Most of positive examples (the area with hotspot occurrences) and negative examples (no hotspot occurrences in the area) that are incorrectly classified by the model are located near rivers and roads.
Starting Page	46
Ending Page	50
File Size	249964
Page Count	5
File Format	PDF
ISBN	9781424498741
ISSN	21570981
e-ISBN	9781424498765
DOI	10.1109/ICTKE.2010.5692912
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-24
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Roads Fires Decision tree method Classification algorithms Rivers C4.5 algorithm Hotspot occurrences Data mining Classification tree analysis
Content Type	Text
Resource Type	Article

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