Estimating dead fine fuel moisture content of forest surface, based on wireless sensor network and back-propagation neural network
Wei-Dong Lei
A B , Ying Yu A , Xing-Hui Li A and Jian Xing A *
+ Author Affiliations
- Author Affiliations
A Department of Information and Computer Engineering, Northeast Forestry University, Harbin, Heilongjiang Province 150040, PR China.
B National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China.
* Correspondence to: xj@nefu.edu.cn
International Journal of Wildland Fire 31(4) 369-378 https://doi.org/10.1071/WF21066
Submitted: 8 July 2020 Accepted: 13 February 2022 Published: 18 March 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF.
Abstract
The dead fine fuel moisture content (FFMC) in forest affects the occurrence and spread of forest fires. Therefore, the estimation of surface dead FFMC plays an important role in forest fire behaviour, fire management and fire danger assessment. However, there are some challenges with current FFMC measurement methods. Modelling using meteorological variables may be a very potential method to achieve remote real-time FFMC estimation. A surface dead FFMC estimation method based on wireless sensor network (WSN) and back-propagation (BP) neural network was proposed. The WSN can realise the acquisition of microclimate data. The BP neural network can use these data to establish multiple FFMC estimation models for different terrain conditions and dead fuel types. The ability of these models to estimate FFMC at four different terrain sampling sites was evaluated. The results suggested that the dead FFMC can be estimated with some degree of accuracy. The correlation coefficients of the estimation results at the four sampling sites were all greater than 0.9, and the mean square errors were all less than 1. The method can be well applied to forest surface dead FFMC estimation and early fire danger assessment, which has practical significance for the rational allocation of fire fighting resources.
Keywords: air temperature, back-propagation neural network, microclimate drivers, precipitation, relative humidity, surface dead fine fuel moisture content, wind speed, wireless sensor network.
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