Measurement and prediction of karstic spring flow ratesView 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>1714784003</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>05</month><year>2024</year></publication_date><journal_volume><volume>17</volume></journal_volume><issue>2</issue></journal_issue><journal_article><titles><title>Measurement and prediction of karstic spring flow rates</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p><div> <p>This paper deals with prediction of the response of karstic springs by means of artificial neural networks (ANNs). A feed-forward back propagation ANN with three layers has been developed, to predict flow rates of two karstic springs, located at Rouvas area, Crete, Greece, using rainfall data as input. While the number of neurons of the input and output layers was determined by choice of data and desired output respectively, the number of neurons of the hidden layer was decided by means of numerous tests. Data used in ANN training and testing include daily and monthly precipitation depths (from September, 2006 to December, 2010) and measured flow rates of the two springs (from April, 2007 to December, 2010). Results show that the trained artificial neural network performed well, although flow rate measurements were not very regular. Moreover, the possibility of estimating the flow rate of one spring, based on measurements of the other has been investigated. Again the ANN gave satisfactory results. All spring flow rate and rainfall measurements are presented as an appendix, to facilitate further scientific research in the area of ANN application to water resources management.</p> </div> <p> </p> </p></abstract><publication_date><month>03</month><day>13</day><year>2015</year></publication_date><pages><first_page>257</first_page><last_page>270</last_page></pages><publisher_item><identifier id_type="pii">gnest_01499</identifier></publisher_item><doi_data><doi>10.30955/gnj.001499</doi><timestamp>1714784003</timestamp><resource>https://journal.gnest.org/publication/gnest_01499</resource></doi_data></journal_article></journal></body></doi_batch> </div>
