Application of artificial neural networks for flood forecasting.View 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>000699</doi_batch_id><timestamp>1714680795</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 3</title></titles><publication_date><month>11</month><year>2024</year></publication_date><journal_volume><volume>6</volume></journal_volume><issue>3</issue></journal_issue><journal_article><titles><title>Application of artificial neural networks for flood forecasting.</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p>In hydrology, as in a number of diverse fields, there has been an increasing use of Artificial Neural Networks (ANN) as black-box simplified models. This is mainly justified by their ability to model complex non-linear patterns; in addition they can self-adjust and produce a consistent response when ‘trained’ using observed outputs. This paper utilises various types of ANNs in an attempt to assess the relative performance of existing models. Ali Efenti, a subcatchment of the river Pinios (Greece), is examined and the results support the hypothesis that ANNs can produce qualitative forecasts. A 7-hour ahead forecast in particular proves to be of fairly high precision, especially when an error prediction technique is introduced to the ANN models.</p></abstract><publication_date><month>04</month><day>29</day><year>2013</year></publication_date><pages><first_page>205</first_page><last_page>211</last_page></pages><publisher_item><identifier id_type="pii">305</identifier></publisher_item><doi_data><doi>10.30955/gnj.000305</doi><timestamp>1714680795</timestamp><resource>https://journal.gnest.org/publication/305</resource></doi_data></journal_article></journal></body></doi_batch> </div>
