Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Alfeu D. Martinho"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-23 (2023)
Abstract Accurate streamflow prediction is essential for efficient water resources management. Machine learning (ML) models are the tools to meet this need. This paper presents a comparative research study focusing on hybridizing ML models with bioin
Externí odkaz:
https://doaj.org/article/4aaf3b8948e544f1964e7517b7e000ba
Publikováno v:
Modeling Earth Systems and Environment. 8:5743-5759
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c4d42a3488949c5f58c825421fd2843
https://doi.org/10.1007/s40808-022-01466-8
https://doi.org/10.1007/s40808-022-01466-8
Publikováno v:
Acta Geophysica. 70:1871-1883
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::adf4a6f1ce7684baf90f3d3460211b60
https://doi.org/10.1007/s11600-022-00834-3
https://doi.org/10.1007/s11600-022-00834-3
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783030711863
ISDA
ISDA
This paper proposes an approach to predicting the monthly natural streamflow at the Cahora Bassa dam, Mozambique, combining a Genetic Algorithm and an Extreme Learning Machine. The model uses measurements of the past monthly river flow, rainfall, tem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7ff01959194154944244c700d454f5d
https://doi.org/10.1007/978-3-030-71187-0_122
https://doi.org/10.1007/978-3-030-71187-0_122
Autor:
Alfeu D. Martinho, Celso Bandeira de Melo Ribeiro, Tales Lima Fonseca, Leonardo Goliatt, Yulia Gorodetskaya
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030637095
BIOMA
BIOMA
This paper proposes a hybrid approach combining an Extreme Learning Machine and a Genetic Algorithm to predict the short-term streamflow at the Cahora Bassa dam, the largest hydroelectric power plant in southern Africa. To predict the streamflows sev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7f9a06ee617f5e7c52fb2da4292f8263
https://doi.org/10.1007/978-3-030-63710-1_20
https://doi.org/10.1007/978-3-030-63710-1_20