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A Perceptron Algorithm for Forest Fire Prediction Based on Wireless Sensor Networks

Haoran Zhu1, Demin Gao1,2,*, Shuo Zhang1

College of Information Science and Technology, Nanjing Forestry University, Nanjing , 210037, China.
Department of Computer Science and Engineering, University of Minnesota. Minneapolis MN, 55455, USA.

*Corresponding Author: Demin Gao. Email: .

Journal on Internet of Things 2019, 1(1), 25-31.


Forest fire prediction constitutes a significant component of forest management. Timely and accurate forest fire prediction will greatly reduce property and natural losses. A quick method to estimate forest fire hazard levels through known climatic conditions could make an effective improvement in forest fire prediction. This paper presents a description and analysis of a forest fire prediction methods based on machine learning, which adopts WSN (Wireless Sensor Networks) technology and perceptron algorithms to provide a reliable and rapid detection of potential forest fire. Weather data are gathered by sensors, and then forwarded to the server, where a fire hazard index can be calculated.


Cite This Article

H. Zhu, D. Gao and S. Zhang, "A perceptron algorithm for forest fire prediction based on wireless sensor networks," Journal on Internet of Things, vol. 1, no.1, pp. 25–31, 2019.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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