{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:55:10Z","timestamp":1771703710239,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:00:00Z","timestamp":1632873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a Multi-Objective Genetic Algorithm (MOGA) framework for the design of Artificial Neural Network (ANN) models is used to design 1-step-ahead prediction models of river water levels. The design procedure is a near-automatic method that, given the data at hand, can partition it into datasets and is able to determine a near-optimal model with the right topology and inputs, offering a good performance on unseen data, i.e., data not used for model design. An example using more than 11 years of water level data (593,178 samples) of the Carri\u00f3n river collected at Villoldo gauge station shows that the MOGA framework can obtain low-complex models with excellent performance on unseen data, achieving an RMSE of 2.5 \u00d7 10\u22123, which compares favorably with results obtained by alternative design.<\/jats:p>","DOI":"10.3390\/s21196504","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"6504","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Optimized Design of Neural Networks for a River Water Level Prediction System"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2627-6393","authenticated-orcid":false,"given":"Miriam L\u00f3pez","family":"Lineros","sequence":"first","affiliation":[{"name":"Design Engineering Department, University of Seville, 41013 Seville, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7881-3912","authenticated-orcid":false,"given":"Antonio Madue\u00f1o","family":"Luna","sequence":"additional","affiliation":[{"name":"Aerospace Engineering and Fluid Mechanical Department, University of Seville, 41013 Seville, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2369-0115","authenticated-orcid":false,"given":"Pedro M.","family":"Ferreira","sequence":"additional","affiliation":[{"name":"LASIGE, Departamento de Inform\u00e1tica, Faculdade de Ci\u00eancias, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6308-8666","authenticated-orcid":false,"given":"Antonio E.","family":"Ruano","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, University of Algarve, 8005-139 Faro, Portugal"},{"name":"IDMEC, Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.tree.2009.03.005","article-title":"Adaptive monitoring: A new paradigm for long-term research and monitoring","volume":"24","author":"Lindenmayer","year":"2009","journal-title":"Trends Ecol. 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