Detection of Outliers and Extreme Events of Ground Level Particulate Matter Using DBSCAN Algorithm with Local Parameters
The critical negative effects of the particulate matter (PM) on human health are proven and hence the studies on the subject are increasing. Besides the health studies vast majority of the researches on particulate matter levels focuses on future projection and forecasting of the particulate matter...
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| Vydáno v: | Water, air, and soil pollution Ročník 233; číslo 6; s. 203 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Cham
Springer International Publishing
01.06.2022
Springer Springer Nature B.V |
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| ISSN: | 0049-6979, 1573-2932 |
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| Abstract | The critical negative effects of the particulate matter (PM) on human health are proven and hence the studies on the subject are increasing. Besides the health studies vast majority of the researches on particulate matter levels focuses on future projection and forecasting of the particulate matter concentrations. The data includes considerable amount of abnormal measurements. To perform an eligible analysis and prediction, a proper outlier analysis process is essential. However the studies focused on outlier identification in PM data are relatively few. This paper focuses on finding outliers and extreme events in ground level PM10 (particles smaller than or equal to 10 μm in diameter) data using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The results show the effectiveness of the method to identify noise and extreme events. |
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| AbstractList | The critical negative effects of the particulate matter (PM) on human health are proven and hence the studies on the subject are increasing. Besides the health studies vast majority of the researches on particulate matter levels focuses on future projection and forecasting of the particulate matter concentrations. The data includes considerable amount of abnormal measurements. To perform an eligible analysis and prediction, a proper outlier analysis process is essential. However the studies focused on outlier identification in PM data are relatively few. This paper focuses on finding outliers and extreme events in ground level PM10 (particles smaller than or equal to 10 μm in diameter) data using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The results show the effectiveness of the method to identify noise and extreme events. The critical negative effects of the particulate matter (PM) on human health are proven and hence the studies on the subject are increasing. Besides the health studies vast majority of the researches on particulate matter levels focuses on future projection and forecasting of the particulate matter concentrations. The data includes considerable amount of abnormal measurements. To perform an eligible analysis and prediction, a proper outlier analysis process is essential. However the studies focused on outlier identification in PM data are relatively few. This paper focuses on finding outliers and extreme events in ground level PM10 (particles smaller than or equal to 10 [mu]m in diameter) data using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The results show the effectiveness of the method to identify noise and extreme events. The critical negative effects of the particulate matter (PM) on human health are proven and hence the studies on the subject are increasing. Besides the health studies vast majority of the researches on particulate matter levels focuses on future projection and forecasting of the particulate matter concentrations. The data includes considerable amount of abnormal measurements. To perform an eligible analysis and prediction, a proper outlier analysis process is essential. However the studies focused on outlier identification in PM data are relatively few. This paper focuses on finding outliers and extreme events in ground level PM10 (particles smaller than or equal to 10 μm in diameter) data using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The results show the effectiveness of the method to identify noise and extreme events. |
| ArticleNumber | 203 |
| Audience | Academic |
| Author | Aslan, Meryem Ezgi Onut, Semih |
| Author_xml | – sequence: 1 givenname: Meryem Ezgi orcidid: 0000-0003-2925-2968 surname: Aslan fullname: Aslan, Meryem Ezgi email: muluhan@yildiz.edu.tr organization: Department of Industrial Engineering, Yıldız Technical University – sequence: 2 givenname: Semih surname: Onut fullname: Onut, Semih organization: Department of Industrial Engineering, Yıldız Technical University |
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| CitedBy_id | crossref_primary_10_3390_math13172812 crossref_primary_10_3390_atmos16030292 crossref_primary_10_2478_ijssis_2024_0004 crossref_primary_10_1061_JPSEA2_PSENG_1589 |
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| Keywords | Air pollution DBSCAN Particulate matter (PM) Noise Extreme events Outlier analysis |
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