Forecasting PM10 levels using ANN and MLR: A case study for Sakarya CityView Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000690</doi_batch_id><timestamp>1713542425</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 2</title></titles><publication_date><month>07</month><year>2024</year></publication_date><journal_volume><volume>20</volume></journal_volume><issue>2</issue></journal_issue><journal_article><titles><title>Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p>In this study, potential of neural network to estimate daily mean PM10 concentration levels in Sakarya city, Turkey as a case study was examined to achieve improved prediction ability. The level and distribution of air pollutants in a particular region is associated with changes in meteorological conditions affecting air movements and topographic features. Thus, meteorological variables data for a two-year period for Sakarya city which is located in most industrialized and crowded part of Turkey were selected as input. Neural network models and multiple linear regression models have been statistically evaluated. The results of the study showed that ANN models were accurate enough for prediction of PM10 levels</p></abstract><publication_date><month>05</month><day>22</day><year>2018</year></publication_date><pages><first_page>281</first_page><last_page>290</last_page></pages><publisher_item><identifier id_type="pii">gnest_02522</identifier></publisher_item><doi_data><doi>10.30955/gnj.002522</doi><timestamp>1713542425</timestamp><resource>https://journal.gnest.org/publication/gnest_02522</resource></doi_data></journal_article></journal></body></doi_batch> </div>
