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Comparison of a black-box model to a traditional numerical model for Hydraulic Head PredictionView 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>000696</doi_batch_id><timestamp>1714129216</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>18</volume></journal_volume><issue>4</issue></journal_issue><journal_article><titles><title>Comparison of a black-box model to a traditional numerical model for Hydraulic Head Prediction</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p><p>Two different methodologies for hydraulic head simulation were compared in this study. The first methodology is a classic numerical groundwater flow simulation model, Princeton Transport Code (PTC), while the second one is a black-box approach that uses Artificial Neural Networks (ANNs). Both methodologies were implemented in the Bavaria region in Germany at thirty observation wells. When using PTC, meteorological and geological data are used in order to compute the simulated hydraulic head following the calibration of the appropriate model parameters. The ANNs use meteorological and hydrological data as input parameters. Different input parameters and ANN architectures were tested and the ANN with the best performance was compared with the PTC model simulation results. One ANN was trained for every observation well and the hydraulic head change was simulated on a daily time step. The performance of the two models was then compared based on the real field data from the study area. The cases in which one model outperforms the other were summarized, while the use of one instead of the other depends on the application and further use of the model. </p> </p></abstract><publication_date><month>10</month><day>19</day><year>2016</year></publication_date><pages><first_page>761</first_page><last_page>770</last_page></pages><publisher_item><identifier id_type="pii">gnest_02002</identifier></publisher_item><doi_data><doi>10.30955/gnj.002002</doi><timestamp>1714129216</timestamp><resource>https://journal.gnest.org/publication/gnest_02002</resource></doi_data></journal_article></journal></body></doi_batch> </div>
