Clustering of Indonesian forest fires using self organizing maps
This paper focuses on clustering the locations of Indonesian forest fires and visualizing them into a two-dimensional map using a self-organizing map (SOM) algorithm. The input data is based on the quantity of the hot spots of forest fires that spread in several locations within ten months period. W...
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| Format: | Article |
| Language: | en |
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Institut Teknologi Brunei
2006
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| Online Access: | http://eprints.utm.my/3099/1/journal-BJTC.pdf http://eprints.utm.my/3099/ http://www.itb.edu.bn/Journal/PastIssues.htm |
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| _version_ | 1845470828447137792 |
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| author | Selamat, Ali Selamat, Md. Hafiz |
| author_facet | Selamat, Ali Selamat, Md. Hafiz |
| author_sort | Selamat, Ali |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This paper focuses on clustering the locations of Indonesian forest fires and visualizing them into a two-dimensional map using a self-organizing map (SOM) algorithm. The input data is based on the quantity of the hot spots of forest fires that spread in several locations within ten months period. We analyze the distributions of the hot spots locations of the regions that may have the high frequencies to risk of the forest fires disaster using the SOM algorithm. We have used a principal component analysis (PCA) to reduce the size of the original datasets in order to improve the accuracy of the clustering results. The SOM algorithm has been used to cluster and visualize the map of the hot spots locations into four groups based on the relative similarity of the risks of forest fires on each of the regions such as danger level, low level, high risks, and low risks. From the analysis we have found that a time period where the highest level of quantity and intensity of the forest fires occurs in some regions can be clearly classified.
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| format | Article |
| id | my.utm.eprints-3099 |
| institution | Universiti Teknologi Malaysia |
| language | en |
| publishDate | 2006 |
| publisher | Institut Teknologi Brunei |
| record_format | eprints |
| spelling | my.utm.eprints-30992011-11-16T05:36:29Z http://eprints.utm.my/3099/ Clustering of Indonesian forest fires using self organizing maps Selamat, Ali Selamat, Md. Hafiz QA76 Computer software This paper focuses on clustering the locations of Indonesian forest fires and visualizing them into a two-dimensional map using a self-organizing map (SOM) algorithm. The input data is based on the quantity of the hot spots of forest fires that spread in several locations within ten months period. We analyze the distributions of the hot spots locations of the regions that may have the high frequencies to risk of the forest fires disaster using the SOM algorithm. We have used a principal component analysis (PCA) to reduce the size of the original datasets in order to improve the accuracy of the clustering results. The SOM algorithm has been used to cluster and visualize the map of the hot spots locations into four groups based on the relative similarity of the risks of forest fires on each of the regions such as danger level, low level, high risks, and low risks. From the analysis we have found that a time period where the highest level of quantity and intensity of the forest fires occurs in some regions can be clearly classified. Institut Teknologi Brunei 2006-01 Article PeerReviewed application/pdf en http://eprints.utm.my/3099/1/journal-BJTC.pdf Selamat, Ali and Selamat, Md. Hafiz (2006) Clustering of Indonesian forest fires using self organizing maps. Brunei Darussalam Journal of Technology and Commerce, 4 (1). pp. 113-120. ISSN 1605-2285 http://www.itb.edu.bn/Journal/PastIssues.htm |
| spellingShingle | QA76 Computer software Selamat, Ali Selamat, Md. Hafiz Clustering of Indonesian forest fires using self organizing maps |
| title | Clustering of Indonesian forest fires using self organizing maps |
| title_full | Clustering of Indonesian forest fires using self organizing maps |
| title_fullStr | Clustering of Indonesian forest fires using self organizing maps |
| title_full_unstemmed | Clustering of Indonesian forest fires using self organizing maps |
| title_short | Clustering of Indonesian forest fires using self organizing maps |
| title_sort | clustering of indonesian forest fires using self organizing maps |
| topic | QA76 Computer software |
| url | http://eprints.utm.my/3099/1/journal-BJTC.pdf http://eprints.utm.my/3099/ http://www.itb.edu.bn/Journal/PastIssues.htm |
| url_provider | http://eprints.utm.my/ |