Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors
Purpose – This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO2 prediction models. The purpose of this paper is to pre-process a relatively large data of NO2 from Internet of Thing (IoT) sensors with time-corresponding weather an...
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| Format: | Article |
| Language: | English |
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Emerald Publishing
2025-01-01
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| Series: | Applied Computing and Informatics |
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| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/ACI-04-2021-0092/full/pdf |
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| author | Habeeb Balogun Hafiz Alaka Christian Nnaemeka Egwim |
| author_facet | Habeeb Balogun Hafiz Alaka Christian Nnaemeka Egwim |
| author_sort | Habeeb Balogun |
| collection | DOAJ |
| description | Purpose – This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO2 prediction models. The purpose of this paper is to pre-process a relatively large data of NO2 from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO2 prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison. Design/methodology/approach – This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist. Findings – The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO2 pollution. Practical implications – This paper's hybrid model provides a basis for giving an informed decision on the NO2 pollutant avoidance system. Originality/value – This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration. |
| format | Article |
| id | doaj-art-aaea98afc77349adb9fe3d950c74e095 |
| institution | Kabale University |
| issn | 2634-1964 2210-8327 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Applied Computing and Informatics |
| spelling | doaj-art-aaea98afc77349adb9fe3d950c74e0952025-01-28T12:19:18ZengEmerald PublishingApplied Computing and Informatics2634-19642210-83272025-01-01211/210111310.1108/ACI-04-2021-0092Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensorsHabeeb Balogun0Hafiz Alaka1Christian Nnaemeka Egwim2Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield, UKBig Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield, UKBig Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield, UKPurpose – This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO2 prediction models. The purpose of this paper is to pre-process a relatively large data of NO2 from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO2 prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison. Design/methodology/approach – This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist. Findings – The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO2 pollution. Practical implications – This paper's hybrid model provides a basis for giving an informed decision on the NO2 pollutant avoidance system. Originality/value – This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration.https://www.emerald.com/insight/content/doi/10.1108/ACI-04-2021-0092/full/pdfIoTBigdataAir pollution predictionHybrid machine learning |
| spellingShingle | Habeeb Balogun Hafiz Alaka Christian Nnaemeka Egwim Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors Applied Computing and Informatics IoT Bigdata Air pollution prediction Hybrid machine learning |
| title | Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors |
| title_full | Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors |
| title_fullStr | Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors |
| title_full_unstemmed | Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors |
| title_short | Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors |
| title_sort | boruta grid search least square support vector machine for no2 pollution prediction using big data analytics and iot emission sensors |
| topic | IoT Bigdata Air pollution prediction Hybrid machine learning |
| url | https://www.emerald.com/insight/content/doi/10.1108/ACI-04-2021-0092/full/pdf |
| work_keys_str_mv | AT habeebbalogun borutagridsearchleastsquaresupportvectormachineforno2pollutionpredictionusingbigdataanalyticsandiotemissionsensors AT hafizalaka borutagridsearchleastsquaresupportvectormachineforno2pollutionpredictionusingbigdataanalyticsandiotemissionsensors AT christiannnaemekaegwim borutagridsearchleastsquaresupportvectormachineforno2pollutionpredictionusingbigdataanalyticsandiotemissionsensors |