Machine Learning-based Calibration Approach for Low-cost Air Pollution Sensors MQ-7 and MQ-131
Air quality is a vital concern globally, and Sri Lanka, according to WHO statistics, faces challenges in achieving optimal air quality levels. To address this, we introduced an innovative IoT-based Air Pollution Monitoring (APM) Box. This solution incorporates readily available Commercial Off-The-Sh...
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| Format: | Article |
| Language: | English |
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Technoscience Publications
2024-03-01
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| Series: | Nature Environment and Pollution Technology |
| Subjects: | |
| Online Access: | https://neptjournal.com/upload-images/(34)D-1457.pdf |
| _version_ | 1797264937587834880 |
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| author | L. R. S. D. Rathnayake, G. B. Sakura, N. A. Weerasekara and P. D. Sandaruwan |
| author_facet | L. R. S. D. Rathnayake, G. B. Sakura, N. A. Weerasekara and P. D. Sandaruwan |
| author_sort | L. R. S. D. Rathnayake, G. B. Sakura, N. A. Weerasekara and P. D. Sandaruwan |
| collection | DOAJ |
| description | Air quality is a vital concern globally, and Sri Lanka, according to WHO statistics, faces challenges in achieving optimal air quality levels. To address this, we introduced an innovative IoT-based Air Pollution Monitoring (APM) Box. This solution incorporates readily available Commercial Off-The-Shelf (COTS) sensors, specifically MQ-7 and MQ-131, for measuring concentrations of Carbon Monoxide (CO) and Ozone (O3) ,Arduino and "ThingSpeak" platform. Yet, those COTS sensors are not factory-calibrated. Therefore, we implemented machine learning algorithms, including linear regression and deep neural network models, to enhance the accuracy of CO and O3 concentration measurements from these non-calibrated sensors. Our findings indicate promising correlations when dealing with MQ-7 and MQ-131 measurements after removing outliers. |
| first_indexed | 2024-04-25T00:36:50Z |
| format | Article |
| id | doaj.art-bee36ebeae524e09846d9b832ba5af19 |
| institution | Directory Open Access Journal |
| issn | 0972-6268 2395-3454 |
| language | English |
| last_indexed | 2024-04-25T00:36:50Z |
| publishDate | 2024-03-01 |
| publisher | Technoscience Publications |
| record_format | Article |
| series | Nature Environment and Pollution Technology |
| spelling | doaj.art-bee36ebeae524e09846d9b832ba5af192024-03-12T16:37:29ZengTechnoscience PublicationsNature Environment and Pollution Technology0972-62682395-34542024-03-0123140140810.46488/NEPT.2024.v23i01.034Machine Learning-based Calibration Approach for Low-cost Air Pollution Sensors MQ-7 and MQ-131L. R. S. D. Rathnayake, G. B. Sakura, N. A. Weerasekara and P. D. SandaruwanAir quality is a vital concern globally, and Sri Lanka, according to WHO statistics, faces challenges in achieving optimal air quality levels. To address this, we introduced an innovative IoT-based Air Pollution Monitoring (APM) Box. This solution incorporates readily available Commercial Off-The-Shelf (COTS) sensors, specifically MQ-7 and MQ-131, for measuring concentrations of Carbon Monoxide (CO) and Ozone (O3) ,Arduino and "ThingSpeak" platform. Yet, those COTS sensors are not factory-calibrated. Therefore, we implemented machine learning algorithms, including linear regression and deep neural network models, to enhance the accuracy of CO and O3 concentration measurements from these non-calibrated sensors. Our findings indicate promising correlations when dealing with MQ-7 and MQ-131 measurements after removing outliers.https://neptjournal.com/upload-images/(34)D-1457.pdfiot, mq-7, mq-131, thingspeak, machine learning, neural network |
| spellingShingle | L. R. S. D. Rathnayake, G. B. Sakura, N. A. Weerasekara and P. D. Sandaruwan Machine Learning-based Calibration Approach for Low-cost Air Pollution Sensors MQ-7 and MQ-131 Nature Environment and Pollution Technology iot, mq-7, mq-131, thingspeak, machine learning, neural network |
| title | Machine Learning-based Calibration Approach for Low-cost Air Pollution Sensors MQ-7 and MQ-131 |
| title_full | Machine Learning-based Calibration Approach for Low-cost Air Pollution Sensors MQ-7 and MQ-131 |
| title_fullStr | Machine Learning-based Calibration Approach for Low-cost Air Pollution Sensors MQ-7 and MQ-131 |
| title_full_unstemmed | Machine Learning-based Calibration Approach for Low-cost Air Pollution Sensors MQ-7 and MQ-131 |
| title_short | Machine Learning-based Calibration Approach for Low-cost Air Pollution Sensors MQ-7 and MQ-131 |
| title_sort | machine learning based calibration approach for low cost air pollution sensors mq 7 and mq 131 |
| topic | iot, mq-7, mq-131, thingspeak, machine learning, neural network |
| url | https://neptjournal.com/upload-images/(34)D-1457.pdf |
| work_keys_str_mv | AT lrsdrathnayakegbsakuranaweerasekaraandpdsandaruwan machinelearningbasedcalibrationapproachforlowcostairpollutionsensorsmq7andmq131 |