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Neural networks approached for modelling river suspended sediment concentration due to tropical stormsView 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>000695</doi_batch_id><timestamp>1713999426</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 4</title></titles><publication_date><month>12</month><year>2024</year></publication_date><journal_volume><volume>11</volume></journal_volume><issue>4</issue></journal_issue><journal_article><titles><title>Neural networks approached for modelling river suspended sediment concentration due to tropical storms</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p>Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available. </p></abstract><publication_date><month>04</month><day>30</day><year>2013</year></publication_date><pages><first_page>457</first_page><last_page>466</last_page></pages><publisher_item><identifier id_type="pii">628</identifier></publisher_item><doi_data><doi>10.30955/gnj.000628</doi><timestamp>1713999426</timestamp><resource>https://journal.gnest.org/publication/628</resource></doi_data></journal_article></journal></body></doi_batch> </div>
