{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:28:13Z","timestamp":1780500493742,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2016,8,4]],"date-time":"2016-08-04T00:00:00Z","timestamp":1470268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["BLX2013009"],"award-info":[{"award-number":["BLX2013009"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["2015ZCQ-GX-03"],"award-info":[{"award-number":["2015ZCQ-GX-03"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31500588"],"award-info":[{"award-number":["31500588"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 (\u226582.5%) than single-sensor input (50.9%\u201392.5%). 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.<\/jats:p>","DOI":"10.3390\/s16081228","type":"journal-article","created":{"date-parts":[[2016,8,4]],"date-time":"2016-08-04T10:17:33Z","timestamp":1470305853000},"page":"1228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network"],"prefix":"10.3390","volume":"16","author":[{"given":"Xiaofei","family":"Yan","sequence":"first","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Agricultural University of Hebei, Baoding 071001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yandong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhua","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoliang","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,8,4]]},"reference":[{"key":"ref_1","unstructured":"Rowell, A., and Moore, P.F. 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