An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning
- Title
- An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning
- Author
- 조병완
- Keywords
- underground coal mines; internet-of-things; azure machine learning; artificial neural network; mine environment index
- Issue Date
- 2018-04
- Publisher
- MDPI
- Citation
- SENSORS, v. 18, no. 4, Article no. 930
- Abstract
- 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 costeffective 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. Arduinobased 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 PCAbased input parameters. The results showed a better performance of the PCAbased ANN for MEI prediction, with R-2 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.
- URI
- https://www.mdpi.com/1424-8220/18/4/930https://repository.hanyang.ac.kr/handle/20.500.11754/118388
- ISSN
- 1424-8220
- DOI
- 10.3390/s18040930; 10.3390/s18040930
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > CIVIL AND ENVIRONMENTAL ENGINEERING(건설환경공학과) > Articles
- Files in This Item:
- An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning.pdfDownload
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