{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T04:13:32Z","timestamp":1773893612571,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"COFAC\/EIGeS"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Forecasting algorithms have been used to support decision making in companies, and it is necessary to apply approaches that facilitate a good forecasting result. The present paper describes assessments based on a combination of different neural network models, tested to forecast steel production in the world. The main goal is to find the best machine learning model that fits the steel production data in the world to make a forecast for a nine-year period. The study is important for understanding the behavior of the models and sensitivity to hyperparameters of convolutional LSTM and GRU recurrent neural networks. The results show that for long-term prediction, the GRU model is easier to train and provides better results. The article contributes to the validation of the use of other variables that are correlated with the steel production variable, thus increasing forecast accuracy.<\/jats:p>","DOI":"10.3390\/app13010178","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T02:55:07Z","timestamp":1672109707000},"page":"178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Forecasting Steel Production in the World\u2014Assessments Based on Shallow and Deep Neural Networks"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1953-7268","authenticated-orcid":false,"given":"Baldu\u00edno C\u00e9sar","family":"Mateus","sequence":"first","affiliation":[{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Lus\u00f3fona University, Campo Grande, 376, 1749-024 Lisboa, Portugal"},{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, 6201-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4313-7966","authenticated-orcid":false,"given":"Mateus","family":"Mendes","sequence":"additional","affiliation":[{"name":"Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal"},{"name":"Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-8079","authenticated-orcid":false,"given":"Jos\u00e9 Torres","family":"Farinha","sequence":"additional","affiliation":[{"name":"Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal"},{"name":"Centre for Mechanical Engineering, Materials and Processes\u2014CEMMPRE, University of Coimbra, 3030-788 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8737-6999","authenticated-orcid":false,"given":"Ant\u00f3nio J. Marques","family":"Cardoso","sequence":"additional","affiliation":[{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, 6201-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1697-055X","authenticated-orcid":false,"given":"Rui","family":"Assis","sequence":"additional","affiliation":[{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Lus\u00f3fona University, Campo Grande, 376, 1749-024 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6138-8593","authenticated-orcid":false,"given":"Luc\u00e9lio M.","family":"da Costa","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Coimbra, 3030-788 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gajdzik, B., Sroka, W., and Vveinhardt, J. 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