Deep Learning-Based River Flow Forecasting with MLPs: Comparative Exploratory Analysis Applied to the Tejo and the Mondego Rivers

dc.contributor.author	Jesus, G.	pt_BR
dc.contributor.author	Korani, Z.	pt_BR
dc.contributor.author	Alves, E.	pt_BR
dc.contributor.author	Oliveira, A.	pt_BR
dc.contributor.editor	MDPI Sensors	pt_BR
dc.date.accessioned	2026-01-21T15:51:35Z	pt_BR
dc.date.accessioned	2026-05-05T16:26:52Z
dc.date.available	2026-01-21T15:51:35Z	pt_BR
dc.date.available	2026-05-05T16:26:52Z
dc.date.issued	2025-03-28	pt_BR
dc.identifier.citation	https://doi.org/ 10.3390/s25072154	pt_BR
dc.identifier.uri	http://dspace2.lnec.pt:8080/jspui/handle/123456789/1019387	pt_BR
dc.identifier.uri	http://repositorio.lnec.pt:8080/jspui/handle/123456789/1019387
dc.language.iso	eng	pt_BR
dc.publisher	MDPI Sensors	pt_BR
dc.rights	openAccess	pt_BR
dc.subject	river flow forecasting	pt_BR
dc.subject	artificial intelligence	pt_BR
dc.subject	deep learning	pt_BR
dc.subject	MLP	pt_BR
dc.subject	SNIRH	pt_BR
dc.title	Deep Learning-Based River Flow Forecasting with MLPs: Comparative Exploratory Analysis Applied to the Tejo and the Mondego Rivers	pt_BR
dc.type	article	pt_BR
dc.description.pages	27p.	pt_BR
dc.description.volume	Vol. 25, Nº 7	pt_BR
dc.description.sector	CICTI/NTIII	pt_BR
dc.description.magazine	MDPI Sensors	pt_BR
dc.contributor.peer-reviewed	SIM	pt_BR
dc.contributor.academicresearchers	SIM	pt_BR
dc.contributor.arquivo	SIM	pt_BR