Next Article in Journal
A Novel One-Step Fabricated, Droplet-Based Electrochemical Sensor for Facile Biochemical Assays
Next Article in Special Issue
Laboratory Performance of Five Selected Soil Moisture Sensors Applying Factory and Own Calibration Equations for Two Soil Media of Different Bulk Density and Salinity Levels
Previous Article in Journal
Frequency Splitting Analysis and Compensation Method for Inductive Wireless Powering of Implantable Biosensors
Previous Article in Special Issue
Impedance of the Grape Berry Cuticle as a Novel Phenotypic Trait to Estimate Resistance to Botrytis Cinerea
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(8), 1228; doi:10.3390/s16081228

Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network

1
School of Technology, Beijing Forestry University, Beijing 100083, China
2
College of Information Science and Technology, Agricultural University of Hebei, Baoding 071001, China
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 19 June 2016 / Revised: 27 July 2016 / Accepted: 28 July 2016 / Published: 4 August 2016
(This article belongs to the Collection Sensors in Agriculture and Forestry)
View Full-Text   |   Download PDF [1297 KB, uploaded 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, CO2, 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, CO2 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 CO2; smoke and temperature; smoke, CO2 and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥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. View Full-Text
Keywords: identification; smoldering combustion; flaming combustion; artificial neural network; ZigBee identification; smoldering combustion; flaming combustion; artificial neural network; ZigBee
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yan, X.; Cheng, H.; Zhao, Y.; Yu, W.; Huang, H.; Zheng, X. Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network. Sensors 2016, 16, 1228.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top