A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy
Air pollution has a significant impact on environment resulting in consequences such as global warming and acid rain. Toxic emissions from vehicles are one of the primary sources of pollution. Assessment of air pollution data is critical in order to assist residents in locating the safest areas in t...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/2893021 |
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| author | Chetan Shetty S. Seema B. J. Sowmya Rajesh Nandalike S. Supreeth Dayananda P. Rohith S. Vishwanath Y. Rajeev Ranjan Venugopal Goud |
| author_facet | Chetan Shetty S. Seema B. J. Sowmya Rajesh Nandalike S. Supreeth Dayananda P. Rohith S. Vishwanath Y. Rajeev Ranjan Venugopal Goud |
| author_sort | Chetan Shetty |
| collection | DOAJ |
| description | Air pollution has a significant impact on environment resulting in consequences such as global warming and acid rain. Toxic emissions from vehicles are one of the primary sources of pollution. Assessment of air pollution data is critical in order to assist residents in locating the safest areas in the city that are ideal for life. In this work, density-based spatial clustering of applications with noise (DBSCAN) is used which is among the widely used clustering algorithms in machine learning. It is not only capable of finding clusters of various sizes and shapes but can also detect outliers. DBSCAN takes in two important input parameters—Epsilon (Eps) and Minimum Points (MinPts). Even the slightest of variations in the parameter values fed to DBSCAN makes a big difference in the clustering. There is a need to find Eps value in as minimum time as possible. In this work, the goal is to find the Eps value in less time. For this purpose, a search tree technique is used for finding the Eps input to the DBSCAN algorithm. Predicting air pollution is a complex task due to various challenges associated with the dynamic and multifaceted nature of the atmosphere such as meteorological variability, local emissions and sources, data quality and availability, and emerging pollutants. Extensive experiments prove that the search tree approach to find Eps is quicker and efficient in comparison to the widely used KNN algorithm. The time reduction to find Eps makes a significant impact as the dataset size increases. The input parameters are fed to DBSCAN algorithm to obtain clustering results. |
| format | Article |
| id | doaj-art-79c7bcb682954bc7b6e0cd9fec2d18ff |
| institution | Kabale University |
| issn | 2314-4912 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-79c7bcb682954bc7b6e0cd9fec2d18ff2025-02-03T01:31:53ZengWileyJournal of Engineering2314-49122024-01-01202410.1155/2024/2893021A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation StrategyChetan Shetty0S. Seema1B. J. Sowmya2Rajesh Nandalike3S. Supreeth4Dayananda P.5Rohith S.6Vishwanath Y.7Rajeev Ranjan8Venugopal Goud9Department of Computer Science and EngineeringDepartment of Computer Science and EngineeringDepartment of Artificial Intelligence and Data ScienceDepartment of Electronics & Communication EngineeringSchool of Computer Science and EngineeringDepartment of Information TechnologyDepartment of Electronics & Communication EngineeringPresidency UniversityGovernment Engineering CollegeG. Pulla Reddy Engineering CollegeAir pollution has a significant impact on environment resulting in consequences such as global warming and acid rain. Toxic emissions from vehicles are one of the primary sources of pollution. Assessment of air pollution data is critical in order to assist residents in locating the safest areas in the city that are ideal for life. In this work, density-based spatial clustering of applications with noise (DBSCAN) is used which is among the widely used clustering algorithms in machine learning. It is not only capable of finding clusters of various sizes and shapes but can also detect outliers. DBSCAN takes in two important input parameters—Epsilon (Eps) and Minimum Points (MinPts). Even the slightest of variations in the parameter values fed to DBSCAN makes a big difference in the clustering. There is a need to find Eps value in as minimum time as possible. In this work, the goal is to find the Eps value in less time. For this purpose, a search tree technique is used for finding the Eps input to the DBSCAN algorithm. Predicting air pollution is a complex task due to various challenges associated with the dynamic and multifaceted nature of the atmosphere such as meteorological variability, local emissions and sources, data quality and availability, and emerging pollutants. Extensive experiments prove that the search tree approach to find Eps is quicker and efficient in comparison to the widely used KNN algorithm. The time reduction to find Eps makes a significant impact as the dataset size increases. The input parameters are fed to DBSCAN algorithm to obtain clustering results.http://dx.doi.org/10.1155/2024/2893021 |
| spellingShingle | Chetan Shetty S. Seema B. J. Sowmya Rajesh Nandalike S. Supreeth Dayananda P. Rohith S. Vishwanath Y. Rajeev Ranjan Venugopal Goud A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy Journal of Engineering |
| title | A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy |
| title_full | A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy |
| title_fullStr | A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy |
| title_full_unstemmed | A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy |
| title_short | A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy |
| title_sort | machine learning approach for environmental assessment on air quality and mitigation strategy |
| url | http://dx.doi.org/10.1155/2024/2893021 |
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