Výsledky vyhledávání - "Victor Costa Porto"

Akademický článek

Enhancing streamflow forecasting for the Brazilian electricity sector: a strategy based on a hyper-multimodel

Autor: Francisco de Assis de Souza Filho, Renan Vieira Rocha, Ályson Brayner Estácio, Larissa Zaira Rafael Rolim, João Dehon de Araújo Pontes Filho, Victor Costa Porto, Sullyandro Oliveira Guimarães

Publikováno v: Revista Brasileira de Recursos Hídricos, Vol 28 (2023)

ABSTRACT Streamflow forecasting plays an important role in ensuring the reliable supply of electricity in countries heavily reliant on hydropower. This paper proposes a novel framework that integrates various hydrological models, climate models, and

Externí odkaz: https://doaj.org/article/d3f2bf5ba5964ea9a22453fd2e115716

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Akademický článek

A framework to evaluate and compare synthetic streamflow scenario generation models

Autor: Felipe Treistman, Débora Dias Jardim Penna, Lucas de Souza Khenayfis, Nelson Bernardo Rodrigues Cavalcante, Francisco de Assis de Souza Filho, Renan Vieira Rocha, Ályson Brayner Estácio, Larissa Zaira Rafael Rolim, João Dehon de Araújo Pontes Filho, Victor Costa Porto, Sullyandro Oliveira Guimarães, José Francisco Moreira Pessanha, Victor Andrade de Almeida, Priscilla Dafne Shu Chan, Thiago Lappicy, Carlos Henrique Ribeiro Lima, Daniel Henrique Marco Detzel, Marcelo Rodrigues Bessa

Publikováno v: Revista Brasileira de Recursos Hídricos, Vol 28 (2023)

ABSTRACT Synthetic streamflow scenario generation is particularly important in countries like Brazil, where hydroelectric power generation plays a key role and properly handling the uncertainty of future streamflow is crucial. This paper showcases a

Externí odkaz: https://doaj.org/article/d870ee0272b345aea3c54c0accaf17b7

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A Hydro-Economic Model to Support Water Scarcity

Autor: Renata Locarno Frota, Samíria Maria Oliveira Silva, Francisco de Assis Souza Filho, Victor Costa Porto

Publikováno v: Water Economics and Policy.

Climate variability is reflected in water affluence that directly impacts the availability and level of water in reservoirs. Economic instruments, such as tariffs, are increasingly being used to promote the rational use of water. This study offers a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ac51e9bc0afc4dbc85579a5108eebd0b
https://doi.org/10.1142/s2382624x22500126

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'Network' socio-hydrology: a case study of causal factors that shape the Jaguaribe River Basin, Ceará-Brazil

Autor: Renan Vieira Rocha, Samiria Maria Oliveira da Silva, Victor Costa Porto, Luis Silva Barros, Renata Locarno Frota, Francisco de Assis de Souza Filho

Publikováno v: Hydrological Sciences Journal. 66:935-950

Socio-hydrology has recently garnered attention as a promising research paradigm among hydrologists. In this paper, we argue that viewing the socio-hydrological systems as a network of causal facto...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::442e3128bb6e72937652e15c6259fe97
https://doi.org/10.1080/02626667.2021.1913282

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Deep Learning for Streamflow Regionalization for Ungauged Basins: Application of Long-Short-Term-Memory Cells in Semiarid Regions

Autor: Francisco José Matos Nogueira Filho, Francisco de Assis Souza Filho, Victor Costa Porto, Renan Vieira Rocha, Ályson Brayner Sousa Estácio, Eduardo Sávio Passos Rodrigues Martins

Publikováno v: Water; Volume 14; Issue 9; Pages: 1318

Rainfall-runoff modeling in ungauged basins continues to be a great hydrological research challenge. A novel approach is the Long-Short-Term-Memory neural network (LSTM) from the Deep Learning toolbox, which few works have addressed its use for rainf

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b795293c69c9324f23e42394c18062ee

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Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil

Autor: Taís Maria Nunes Carvalho, Francisco de Assis de Souza Filho, Victor Costa Porto

Publikováno v: Journal of Water Resources Planning and Management. 147

Despite recent efforts to apply machine learning (ML) for water demand modeling, overcoming the black-box nature of these techniques to extract practical information remains a challenge, es...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::98c833c8b90e5e1827eaed871bbce088
https://doi.org/10.1061/(asce)wr.1943-5452.0001310

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