Publicación:
Hybrid models based on mode decomposition and recurrent neural networks for streamflow forecasting in the Chira river in Peru

dc.contributor.author La Rosa Lama G. es_PE
dc.contributor.author Sanchez I. es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2020
dc.description.abstract Streamflow forecasting at short horizons is vital for the management of water resources. However, the streamflow behaviour is non-linear and not stationary. To address this challenge, artificial intelligence techniques have been used to increase accuracy. Additionally, signal decomposition techniques such as empirical mode decomposition, ensemble empirical mode decomposition, and variational mode decomposition, have been applied in different fields as a pre-processing stage prior to modelling to improve forecasting. This study evaluates the effect of the aforementioned decomposition techniques used with a recurrent neural network called long short-Term memory to increase the precision of the daily prediction of the Chira river streamflow in northern Peru, characterized by a special dynamic due to a strong seasonal behavior and the influence of the El Niño-Southern Oscillation (ENSO). © 2020 IEEE.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1109/EIRCON51178.2020.9254035
dc.identifier.scopus 2-s2.0-85097816504
dc.identifier.uri https://hdl.handle.net/20.500.12390/2470
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020
dc.rights info:eu-repo/semantics/openAccess
dc.subject streamflow forecasting
dc.subject LSTM es_PE
dc.subject mode decomposition signal es_PE
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#2.02.04
dc.title Hybrid models based on mode decomposition and recurrent neural networks for streamflow forecasting in the Chira river in Peru
dc.type info:eu-repo/semantics/article
dspace.entity.type Publication
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