Abstract
Forest fire is a disaster that causes economic and ecological damage and human life threat. Thus predicting such critical environmental issue is essential to mitigate this threat. In this paper we propose a decision tree based system for forest fire prediction. The aim being the integration of the decision tree classifier as a part of the smart sensor node architecture that allows fire prediction in automated and intelligent way without requiring human intervention. The fire prediction is based on the meteorological data corresponding to the critical weather elements that influence the forest fire occurrence, namely temperature, relative humidity and wind speed. We have obtained accuracy about 82.92% regarding the software implementation of the proposed DT based forest fire prediction system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Benkheira, A.: Ministère de l’agriculture et de développement rurale et de la pèche, direction des forets Les feux de forêts en Algérie analyse et perspectives. Technical report (2018)
San-Miguel-Ayanz, J., Durrant, T., Boca, R., Libertà, G., Branco, A., de Rigo, D., Ferrari, D., Maianti, P., Vivancos, T.A., Costa, H., Lana, F.: Advance EFFIS report on Forest Fires in Europe, Middle East and North Africa (2017)
Maksimović, M., Vujović, V.: Comparative analysis of data mining techniques applied to wireless sensor network data for fire detection. J. Inf. Technol. Appl. (JITA) 3(2), 65–773 (2013)
Giuntini, F.T., Beder, D.M., Ueyama, J.: Exploiting self-organization and fault tolerance in wireless sensor networks: a case study on wildfire detection application. Int. J. Distrib. Sens. Netw. 13(4), 1–16 (2017)
Bahrepour, M., Van der Zwaag, B.J., Meratnia, N., Havinga, P.: Fire data analysis and feature reduction using computational intelligence methods. In: Phillips-Wren, G., Jain, L.C., Nakamatsu, K., Howlett, R.J. (eds.) Advances in Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol. 4, pp. 289–298. Springer, Heidelberg (2010)
Saoudi, M., Bounceur, A., Euler, R., Kechadi, T.: Data mining techniques applied to wireless sensor networks for early forest fire detection. In: International Conference on Internet of Things and Cloud Computing (ICC), Cambridge, United Kingdom (2016)
Hefeeda, M., Bagheri, M.: Wireless sensor networks for early detection of forest. In: The IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Pisa, Italy, pp. 1–6 (2007)
Liu, Y., Gu, Y., Chen, G., Ji, Y., Li, J.: A novel accurate forest fire detection system using wireless sensor networks. In: IEEE Seventh International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), New York, pp. 52–59 (2011)
Han, J., Kamber, M., Pei, J.: Data Mining Concepts and Techniques, 3rd edn. Elsevier, Amsterdam (2012)
Stojanova, D., Panov, P., Kobler, A., Džeroski, S., Taškova, K.: Learning to predict forest fires with different data mining techniques. In: The 9th International Multi Conference Information Society, Jubljana, Slovinia (2006)
Karouni, A., Daya, B., Chauvet, P.: Applying decision tree algorithm and neural networks to predict forest fires in Lebanon. J. Theor. Appl. Inf. Technol. 63(2), 282–291 (2014)
Cortez, P., Morais, A.: Data mining approach to predict forest fires using meteorological data. In: Neves, J., Santos, M.F., Machado, J. (eds.) New Trends in Artificial Intelligence, 13th Portuguese Conference on Artificial Intelligence (EPIA), Guimaraes, Portugal, pp. 512–523 (2007)
Weka Tool. https://www.cs.waikato.ac.nz/ml/weka/
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Acknowledgments
This work has been done under the socio-economic project No. 14/CDTA/DGRSDT/2017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Abid, F., Izeboudjen, N. (2020). Predicting Forest Fire in Algeria Using Data Mining Techniques: Case Study of the Decision Tree Algorithm. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1105. Springer, Cham. https://doi.org/10.1007/978-3-030-36674-2_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-36674-2_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36673-5
Online ISBN: 978-3-030-36674-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)