Assessing the reliability of a physical-based model and a convolutional neural network in an ungauged watershed for daily streamflow calculation: a case study in southern Portugal

• Published in: Environmental earth sciences Vol. 83; no. 7; p. 215
• Main Authors: Oliveira, Ana R., Ramos, Tiago B., Simionesei, Lucian, Neves, Ramiro
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024; Springer Nature B.V
• Subjects: Artificial neural networks; Biogeosciences; case studies; decision making; Decision support systems; Dry periods; Earth and Environmental Science; Earth Sciences; Environmental Science and Engineering; Geochemistry; Geology; Hydrology/Water Resources; Inflow; Mass balance; Modelling; Neural networks; Original Article; Portugal; Reliability analysis; Reservoirs; Stream discharge; Stream flow; Terrestrial Pollution; Water availability; Water management; Watersheds; Portugal; 1D-CNN; Reservoir inflow; Streamflow; Ungauged estimation; MOHID-Land

The main goal of this study was to estimate inflows to the Maranhão reservoir, southern Portugal, using two distinct modeling approaches: a one-dimensional convolutional neural network (1D-CNN) model and a physically based model. The 1D-CNN was previously trained, validated, and tested in a sub-basi...