Article
Real-Time Identification of Smoldering and Flaming
Combustion Phases in Forest Using a Wireless Sensor
Network-Based Multi-Sensor System and Artificial
Neural Network
Xiaofei Yan
1
, Hong Cheng
2
, Yandong Zhao
1,
*, Wenhua Yu
1
, Huan Huang
1
and
Xiaoliang Zheng
1
1
School of Technology, Beijing Forestry University, Beijing 100083, China; yanxiaofei_21@163.com (X.Y.);
yuwenhua56@sina.com (W.Y.); huan2020@foxmail.com (H.H.); valin912@gmail.com (X.Z.)
2
College of Information Science and Technology, Agricultural University of Hebei, Baoding 071001, China;
chenghong@cau.edu.cn
* Correspondence: yandongzh@bjfu.edu.cn; Tel.: +86-10-6233-7736
Academic Editor: Gonzalo Pajares Martinsanz
Received: 19 June 2016; Accepted: 28 July 2016; Published: 4 August 2016
Abstract:
Diverse sensing techniques have been developed and combined with machine learning
method for forest fire detection, but none of them referred to identifying smoldering and flaming
combustion phases. This study attempts to real-time identify different combustion phases using a
developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network
(ANN). Sensors (CO, CO
2
, smoke, air temperature and relative humidity) were integrated into one
node of WSN. An experiment was conducted using burning materials from residual of forest to
test responses of each node under no, smoldering-dominated and flaming-dominated combustion
conditions. The results showed that the five sensors have reasonable responses to artificial forest
fire. To reduce cost of the nodes, smoke, CO
2
and temperature sensors were chiefly selected through
correlation analysis. For achieving higher identification rate, an ANN model was built and trained
with inputs of four sensor groups: smoke; smoke and CO
2
; smoke and temperature; smoke, CO
2
and temperature. The model test results showed that multi-sensor input yielded higher predicting
accuracy (
ě
82.5%) than single-sensor input (50.9%–92.5%). Based on these, it is possible to reduce the
cost with a relatively high fire identification rate and potential application of the system can be tested
in future under real forest condition.
Keywords:
identification; smoldering combustion; flaming combustion; artificial neural network; ZigBee
1. Introduction
Forest fire has occurred in different regions of the world [
1
], which entails greenhouse gas emission,
pollution and water contamination as well as loss of nutrients and ground microorganisms [2]. Thus,
early detection of forest fire is of importance to decrease the loss of natural resource and economical
cost. On the other hand, high accurate identification of combustion phases can be of benefit to users
for predicting spread direction and speed of forest fire.
Human observation is a traditional method to detect forest fire, but perilous conditions when
fire occurred make people flinching. Thus, various novel sensing technologies and tools have been
developed instead of human observation of forest fire, such as machine vision-based charge-coupled
device (CCD) cameras and infrared (IR) detectors, lidar detection technique, satellite-based remote
sensing, wireless sensor networks, etc.
Machine vision method can monitor variation of fire or smoke in forest and report it to a
control center [
3
,
4
]. The accuracy of machine vision-based system is highly disturbed by terrain,
Sensors 2016, 16, 1228; doi:10.3390/s16081228 www.mdpi.com/journal/sensors
