Robust machine intelligence for learning particulate matter variation around power complex
| dc.authorid | Tutmez, Bulent/0000-0002-2618-3285 | |
| dc.contributor.author | Tutmez, Bulent | |
| dc.date.accessioned | 2024-08-04T20:53:12Z | |
| dc.date.available | 2024-08-04T20:53:12Z | |
| dc.date.issued | 2023 | |
| dc.department | İnönü Üniversitesi | en_US |
| dc.description.abstract | Beyond the possible benefits of coal-based energy generation, there are also increased environmental concerns. Particulate matter (PM) is one of the most important indicators of the severity of air pollution since inhaling particles from coal-fired power generation can be harmful to both the environment and human health. This research aims to appraise relationships between PM and other measurable air quality parameters used as critical information in prevention and control steps. For this purpose, a statistical learning model to estimate PM10 in ambient air was established. A generalized flowchart was created in order to arrive at a generalizable and comprehensive modeling scheme for analyzing air pollution time series data. The main part of the chart, the partial robust regression algorithm aims to provide a reproducible model based on statistical precision, robustness and generality. The comparative experiments performed with heterogeneous data revealed that the suggested robust regression algorithm has better estimation capacity and generality compared with the conventional methods. The results also showed that machine intelligence-based partial robust regression is not very sensitive to data that deviate from normality. | en_US |
| dc.identifier.doi | 10.1007/s40808-022-01612-2 | |
| dc.identifier.endpage | 2150 | en_US |
| dc.identifier.issn | 2363-6203 | |
| dc.identifier.issn | 2363-6211 | |
| dc.identifier.issue | 2 | en_US |
| dc.identifier.scopus | 2-s2.0-85143232382 | en_US |
| dc.identifier.scopusquality | Q1 | en_US |
| dc.identifier.startpage | 2141 | en_US |
| dc.identifier.uri | https://doi.org/10.1007/s40808-022-01612-2 | |
| dc.identifier.uri | https://hdl.handle.net/11616/101032 | |
| dc.identifier.volume | 9 | en_US |
| dc.identifier.wos | WOS:000912730900001 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Springer Heidelberg | en_US |
| dc.relation.ispartof | Modeling Earth Systems and Environment | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Air pollution | en_US |
| dc.subject | Particulate matter | en_US |
| dc.subject | Coal-fired power plant | en_US |
| dc.subject | Robust learning | en_US |
| dc.subject | Partial regression | en_US |
| dc.title | Robust machine intelligence for learning particulate matter variation around power complex | en_US |
| dc.type | Article | en_US |
