Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.
Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.
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利用基于无线传感器网络的多传感器系统和人工神经网络实时识别森林阴燃和明火燃烧阶段
DOI：
10.3390/s16081228
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发表时间：
2016-08-04
期刊：
影响因子：
--
通讯作者：
Zheng X
中科院分区：
文献类型：
--
作者：
Yan X;Cheng H;Zhao Y;Yu W;Huang H;Zheng X
森林火灾探测已经开发了多种传感技术，并与机器学习方法相结合，但没有一种涉及识别阴燃和火焰燃烧阶段。本研究尝试使用一种基于无线传感器网络（WSN）的多传感器系统和人工神经网络（ANN）来实时识别不同的燃烧阶段。将传感器（CO、CO2、烟雾、空气温度和相对湿度）集成到WSN的一个节点中。利用森林残余物的燃烧材料，测试了各节点在无阴燃、有焰燃和无阴燃条件下的响应。结果表明，5种传感器对人工森林火灾具有合理的响应。为了降低节点成本，通过相关性分析，主要选择烟雾、CO2和温度传感器。为了获得更高的识别率，建立了一个人工神经网络模型，并使用四组传感器输入进行训练：烟雾；烟雾和二氧化碳；烟雾和温度；烟雾，二氧化碳和温度。模型检验结果表明，多传感器输入的预测精度（≥82.5%）高于单传感器输入（50.9% ~ 92.5%）。在此基础上，可以降低成本，具有较高的火灾识别率，并可以在未来的真实森林条件下测试系统的潜在应用。
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.
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DOI：
10.1016/j.jag.2009.03.003
发表时间：
2009-08-01
影响因子：
7.5
作者：
Maeda, Eduardo Eiji;Formaggio, Antonio Roberto;Hansen, Matthew C.
通讯作者：
Hansen, Matthew C.
影响因子：
4.5
作者：
Vicente, J;Guillemant, P
通讯作者：
Guillemant, P
影响因子：
8.3
作者：
Blackard, JA;Dean, DJ
通讯作者：
Dean, DJ
DOI：
10.3390/s90604465
发表时间：
2009
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Tsiourlis G;Andreadakis S;Konstantinidis P
通讯作者：
Konstantinidis P
影响因子：
5.1
作者：
Töreyin, BU;Dedeoglu, Y;Çetin, AE
通讯作者：
Çetin, AE