An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning
Department of Civil and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea
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Sensors 2018, 18(4), 930; https://doi.org/10.3390/s18040930
Received: 20 February 2018
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Revised: 15 March 2018
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Accepted: 16 March 2018
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Published: 21 March 2018
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.
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Keywords:
underground coal mines; internet-of-things; azure machine learning; artificial neural network; mine environment index
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MDPI and ACS Style
Jo, B.; Khan, R.M.A. An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning. Sensors 2018, 18, 930. https://doi.org/10.3390/s18040930
AMA Style
Jo B, Khan RMA. An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning. Sensors. 2018; 18(4):930. https://doi.org/10.3390/s18040930
Chicago/Turabian StyleJo, ByungWan, and Rana M.A. Khan. 2018. "An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning" Sensors 18, no. 4: 930. https://doi.org/10.3390/s18040930
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