An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning
Problem
Underground coal mines need air quality monitoring that goes beyond passive sensing to assess hazards and predict pollutant levels to protect miners.
Why it matters
Toxic mine gases directly threaten miners' lives, and prior smart systems could only monitor or report events.
What the AI did
An IoT system of Arduino sensor modules feeding a base station running Azure Machine Learning Studio, which assessed air quality via a mine environment index and predicted pollutants; PCA identified the most influencing gases.
Results
PCA identified CH4, CO, SO2, and H2S as most influencing gases; prediction accuracy not stated (abstract truncated)
Impact
System installed in an operational underground coal mine, adding assessment and prediction to monitoring; accuracy unverified in abstract.
Record
| Venue | Sensors |
|---|---|
| Published | 2018 |
| Work type | article |
| Authors | Byung-Wan Jo, Rana Muhammad Asad Khan |
| Citations | 104 (OpenAlex, at import time) |
| References | 41 |
| Model or method | Azure Machine Learning (AML) Studio |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/s18040930 |
| Open access | gold |
| Keywords | IoT, Azure Machine Learning, air quality, underground coal mine, pollutant prediction, wireless sensor networks |
Importance rating
real but narrower advance
Mine gas hazards threaten miner safety and system was installed in an operational underground coal mine, adding assessment and prediction to monitoring; scale of mines/miners affected not quantified.
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