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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: email.

Journal on Internet of Things 2019, 1(1), 25-31. https://doi.org/10.32604/jiot.2019.05897

Abstract

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.

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APA Style
Zhu, H., Gao, D., Zhang, S. (2019). A perceptron algorithm for forest fire prediction based on wireless sensor networks. Journal on Internet of Things, 1(1), 25-31. https://doi.org/10.32604/jiot.2019.05897
Vancouver Style
Zhu H, Gao D, Zhang S. A perceptron algorithm for forest fire prediction based on wireless sensor networks. J Internet Things . 2019;1(1):25-31 https://doi.org/10.32604/jiot.2019.05897
IEEE Style
H. Zhu, D. Gao, and S. Zhang "A Perceptron Algorithm for Forest Fire Prediction Based on Wireless Sensor Networks," J. Internet Things , vol. 1, no. 1, pp. 25-31. 2019. https://doi.org/10.32604/jiot.2019.05897

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cc 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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