Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis
Air pollution emerges as a formidable threat to both public health and environmental integrity, especially in regions undergoing rapid development. In this context, Gujarat, situated in Western India, grapples with escalating air quality degradation attributable to industrialization, vehicular emiss...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
REA Press
2024-12-01
|
| Series: | Big Data and Computing Visions |
| Subjects: | |
| Online Access: | https://www.bidacv.com/article_208453_1ad3a96c5e7a37ecb0032498e33cb5bc.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832579293889167360 |
|---|---|
| author | Gaddam Advitha Allada Nagasai Varaprasad Koti Vennela Khushi Pullabhotla Vijay Sukanta Nayak |
| author_facet | Gaddam Advitha Allada Nagasai Varaprasad Koti Vennela Khushi Pullabhotla Vijay Sukanta Nayak |
| author_sort | Gaddam Advitha |
| collection | DOAJ |
| description | Air pollution emerges as a formidable threat to both public health and environmental integrity, especially in regions undergoing rapid development. In this context, Gujarat, situated in Western India, grapples with escalating air quality degradation attributable to industrialization, vehicular emissions, and agricultural practices. The imperative to accurately forecast Air Quality Indices (AQIs) becomes paramount for the prompt implementation of mitigation measures, thereby safeguarding public well-being. This research delves into the utilization of Machine Learning (ML) algorithms, specifically Random Forest (RF) and XGBoost, to predict AQIs in Gujarat. Four pivotal parameters, PM2.5, PM10, SO2, and NOX, are scrutinized due to their substantial impact on air quality and inclusion in publicly available datasets. Remarkably, both RF and XGBoost models exhibit outstanding performance, surpassing 99% accuracy. This exceptional capability underscores the transformative potential of ML in addressing the complex challenges posed by air pollution. Leveraging the precise predictions of AQI values, these models can catalyze the development of robust early warning systems and guide policymaking endeavors directed at enhancing air quality. Notably, this investigation unveils PM2.5 and PM10 as primary culprits influencing AQI levels in Gujarat, underscoring the urgency for stringent emission control measures targeting these pollutants. Furthermore, the study sheds light on the significant impact of meteorological factors, especially maximum temperature, on AQI fluctuations, necessitating adaptive strategies to counteract climate change's repercussions on air quality. |
| format | Article |
| id | doaj-art-5ccc50d8b7574a4a83c18640ab9bc223 |
| institution | Kabale University |
| issn | 2783-4956 2821-014X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | REA Press |
| record_format | Article |
| series | Big Data and Computing Visions |
| spelling | doaj-art-5ccc50d8b7574a4a83c18640ab9bc2232025-01-30T12:23:44ZengREA PressBig Data and Computing Visions2783-49562821-014X2024-12-014432633910.22105/bdcv.2024.485515.1213208453Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysisGaddam Advitha0Allada Nagasai Varaprasad1Koti Vennela Khushi2Pullabhotla Vijay3Sukanta Nayak4School of Computer Science and Engineering, VIT–AP University, Inavolu, Beside AP Secretariat, Amaravati AP, India.School of Computer Science and Engineering, VIT–AP University, Inavolu, Beside AP Secretariat, Amaravati AP, India.School of Computer Science and Engineering, VIT–AP University, Inavolu, Beside AP Secretariat, Amaravati AP, India.School of Computer Science and Engineering, VIT–AP University, Inavolu, Beside AP Secretariat, Amaravati AP, India.Department of Mathematics, School of Advanced Scineces, VIT – AP University, Inavolu, Beside AP Secretariat, Amaravati AP, India.Air pollution emerges as a formidable threat to both public health and environmental integrity, especially in regions undergoing rapid development. In this context, Gujarat, situated in Western India, grapples with escalating air quality degradation attributable to industrialization, vehicular emissions, and agricultural practices. The imperative to accurately forecast Air Quality Indices (AQIs) becomes paramount for the prompt implementation of mitigation measures, thereby safeguarding public well-being. This research delves into the utilization of Machine Learning (ML) algorithms, specifically Random Forest (RF) and XGBoost, to predict AQIs in Gujarat. Four pivotal parameters, PM2.5, PM10, SO2, and NOX, are scrutinized due to their substantial impact on air quality and inclusion in publicly available datasets. Remarkably, both RF and XGBoost models exhibit outstanding performance, surpassing 99% accuracy. This exceptional capability underscores the transformative potential of ML in addressing the complex challenges posed by air pollution. Leveraging the precise predictions of AQI values, these models can catalyze the development of robust early warning systems and guide policymaking endeavors directed at enhancing air quality. Notably, this investigation unveils PM2.5 and PM10 as primary culprits influencing AQI levels in Gujarat, underscoring the urgency for stringent emission control measures targeting these pollutants. Furthermore, the study sheds light on the significant impact of meteorological factors, especially maximum temperature, on AQI fluctuations, necessitating adaptive strategies to counteract climate change's repercussions on air quality.https://www.bidacv.com/article_208453_1ad3a96c5e7a37ecb0032498e33cb5bc.pdfair qualitymachine learningrandom forestxgboostaqi prediction |
| spellingShingle | Gaddam Advitha Allada Nagasai Varaprasad Koti Vennela Khushi Pullabhotla Vijay Sukanta Nayak Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis Big Data and Computing Visions air quality machine learning random forest xgboost aqi prediction |
| title | Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis |
| title_full | Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis |
| title_fullStr | Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis |
| title_full_unstemmed | Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis |
| title_short | Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis |
| title_sort | cultivating clean skies unveiling the tapestry of air quality in gujarat through innovative machine learning analysis |
| topic | air quality machine learning random forest xgboost aqi prediction |
| url | https://www.bidacv.com/article_208453_1ad3a96c5e7a37ecb0032498e33cb5bc.pdf |
| work_keys_str_mv | AT gaddamadvitha cultivatingcleanskiesunveilingthetapestryofairqualityingujaratthroughinnovativemachinelearninganalysis AT alladanagasaivaraprasad cultivatingcleanskiesunveilingthetapestryofairqualityingujaratthroughinnovativemachinelearninganalysis AT kotivennelakhushi cultivatingcleanskiesunveilingthetapestryofairqualityingujaratthroughinnovativemachinelearninganalysis AT pullabhotlavijay cultivatingcleanskiesunveilingthetapestryofairqualityingujaratthroughinnovativemachinelearninganalysis AT sukantanayak cultivatingcleanskiesunveilingthetapestryofairqualityingujaratthroughinnovativemachinelearninganalysis |