Hotspot occurences classififcation using decision tree method : case study in the Rokan Hilir, Riau Province, Indonesia

Imas, Sukaesih Sitanggang and Ismail, Mohd Hasmadi (2010) Hotspot occurences classififcation using decision tree method : case study in the Rokan Hilir, Riau Province, Indonesia. In: 8th International Conference on ICT and Knowledge Engineering, 24-25 Nov. 2010, Bangkok, Thailand.

Full text not available from this repository.

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.

Item Type:Conference or Workshop Item (Paper)
Keyword:C4.5 algorithm; Decision tree method; Hotspot occurrences
Faculty or Institute:Faculty of Forestry
Publisher:IEEE
DOI Number:10.1109/ICTKE.2010.5692912
Altmetrics:http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICTKE.2010.5692912
ID Code:9326
Deposited By: Samsida Samsudin
Deposited On:24 Jan 2011 09:31
Last Modified:04 Dec 2014 04:48

Repository Staff Only: Edit item detail


Universiti Putra Malaysia Institutional Repository

Universiti Putra Malaysia Institutional Repository is an on-line digital archive that serves as a central collection and storage of scientific information and research at the Universiti Putra Malaysia.

Currently, the collections deposited in the IR consists of Master and PhD theses, Master and PhD Project Report, Journal Articles, Journal Bulletins, Conference Papers, UPM News, Newspaper Cuttings, Patents and Inaugural Lectures.

As the policy of the university does not permit users to view thesis in full text, access is only given to the first 24 pages only.