Machine Learning-based Calibration Approach for Low-cost Air Pollution Sensors MQ-7 and MQ-131
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| Gepubliceerd in: | Nature Environment and Pollution Technology vol. 23, no. 1 (Mar 2024), p. 401 |
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| Hoofdauteur: | |
| Andere auteurs: | , , |
| Gepubliceerd in: |
Technoscience Publications
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| Online toegang: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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|---|---|---|---|
| 001 | 2957757711 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0972-6268 | ||
| 022 | |a 2395-3454 | ||
| 022 | |a 0971-4871 | ||
| 024 | 7 | |a 10.46488/NEPT.2024.v23i01.034 |2 doi | |
| 035 | |a 2957757711 | ||
| 045 | 2 | |b d20240301 |b d20240331 | |
| 084 | |a 179233 |2 nlm | ||
| 100 | 1 | |a Rathnayake, L R S D |u Civil and Environmental Department, Faculty of Technology, University of Sri Jayewardenepura, Sri Lanka | |
| 245 | 1 | |a Machine Learning-based Calibration Approach for Low-cost Air Pollution Sensors MQ-7 and MQ-131 | |
| 260 | |b Technoscience Publications |c Mar 2024 | ||
| 513 | |a Feature | ||
| 520 | 3 | |a 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 loT-based Air Pollution Monitoring (ARM) 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 (03) ,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 03 concentration measurements from these non-calibrated sensors. Our findings indicate promising correlations when dealing with MQ-7 and MQ-131 measurements after removing outliers. | |
| 651 | 4 | |a Sri Lanka | |
| 653 | |a Outliers (statistics) | ||
| 653 | |a Humidity | ||
| 653 | |a Regression analysis | ||
| 653 | |a Sensors | ||
| 653 | |a Carbon monoxide | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Calibration | ||
| 653 | |a Air quality | ||
| 653 | |a Pollution monitoring | ||
| 653 | |a Air pollution | ||
| 653 | |a Machine learning | ||
| 653 | |a Outdoor air quality | ||
| 653 | |a Air monitoring | ||
| 653 | |a Visualization | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Neural networks | ||
| 653 | |a Variables | ||
| 653 | |a Algorithms | ||
| 653 | |a Economic | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Sakura, G B |u Civil and Environmental Department, Faculty of Technology, University of Sri Jayewardenepura, Sri Lanka | |
| 700 | 1 | |a Weerasekara, N A |u Civil and Environmental Department, Faculty of Technology, University of Sri Jayewardenepura, Sri Lanka | |
| 700 | 1 | |a Sandaruwan, P D |u Department of Computer Science, University of Ruhuna, Matara, Sri Lanka | |
| 773 | 0 | |t Nature Environment and Pollution Technology |g vol. 23, no. 1 (Mar 2024), p. 401 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2957757711/abstract/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2957757711/fulltext/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2957757711/fulltextPDF/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |