The week's pick
Reseach Article
Wild Conservation Burnt Area Prediction by Data Mining
| 2nd National Conference on Information and Communication Technology |
| Foundation of Computer Science USA |
| NCICT - Number 5 |
| November 2011 |
| Authors: Divya T.L, Dr. Vijayalakshmi M.N |
| 4ea5c8b2-3035-40b5-b927-8b7910d086c0 |
Divya T.L, Dr. Vijayalakshmi M.N . Wild Conservation Burnt Area Prediction by Data Mining. 2nd National Conference on Information and Communication Technology. NCICT, 5 (November 2011), 9-11.
Abstract
Forest fire is major risk in mediterranean forest area. During summer high temperature and low humidity will cause this problem in wild conservation space. Forest and uncultivated land fires have been witnessed in the past for loss of soil nutrient. Forest Fires is a source of trouble for a long time. Fires for larger hectare have considerable pressure over the ecological system [2] .It is required to calculate the loss of land area due to forest fire, which further helps forest management in identification of soil erosion and loss of wild life in particular area. General Unary Hypotheses Automaton (GUHA) is used to predict the forest land area burnt in hectare. Rules are generated on meteorological conditions. The patterns are recognized using temperature, rainfall and wind speed. This paper intends to identify the item set of weather relation with the size of forest land area in hectare. We have used only three meteorological set of data to predict the loss of land area. The item set are classified into rules for decision making.
References
- Ku Ruhana Ku-Mahamud,Khor Jia Yun ,Pattern Extraction and Rule Generation of Forest Fire Using Sliding Window Techique,international journal of Computer and Information Science,Vol.2,No.3,Aug-2009.
- K.Angayarkkani, Dr.N.Radhakrishnan An Intelligent System For Effective Forest Fire Detection Using Spatial Data ,(IJCSIS) International Journal of Computer Science and Information Security,Vol. 7, No. 1, 2010.
- Data Mining Introductory and Advanced Topics by Margaret H.Dunham, Association Rules pg no178-205, and Spatial mining pg no 235-255, low price edition, Pearson Education
- Aggraval, R. et al.: Fast Discovery of Association Rules. In Fayyad, U.M. et al.: Advances in Knowledge Discovery and Data Mining, pp. 307-328, AAAI Press / MIT Press, 1996
- Relational Data Mining and GUHA, Tom´aˇs Karban, K. Richta, V. Sn´aˇsel, J. Pokorn´y (Eds.): Dateso 2005, pp. 103– 112, ISBN 80-01-03204-3. S.Nandagopal, S.Karthik, V.P.Arunachalam,”Mining of Meteorological Data Using Modified Apriori Algorithm”, European Journal of Scientific Research, ISSN 1450-216X Vol.47 No.2 (2010), pp.295-308.
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