Intelligent Modeling for Streamflow Forecasting

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@Article{OliveiraBrito:2018:ieeeLAT,

• author = "Bethania {Oliveira Brito} and Ricardo {Menezes Salgado} and Luiz {Alberto Beijo}",
• journal = "IEEE Latin America Transactions",
• title = "Intelligent Modeling for Streamflow Forecasting",
• year = "2016",
• volume = "14",
• number = "8",
• pages = "3669--3677",
• abstract = "In Brazil, electrical power generation stems mostly from hydroelectric power plants and this is due to the available geographical conditions. For optimization purposes and economy of these resources, stream flow forecasting is an alternative that helps in the planning of operations in hydroelectric power plants. It is proposed in this paper build models that are able to combine several forecasters, in order to get forecasts with minor errors and therefore more accurate. For this work, we used a base National Systems Operator data (ONS) of some plants that make up the Grande River. The strategy of combining forecasts obtained through different models show excellent results being a promising technique for streamflow forecasting.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/TLA.2016.7786349",
• ISSN = "1548-0992",
• month = aug,
• notes = "Also known as \cite{7786349}",

}

Genetic Programming entries for Bethania Oliveira Brito Ricardo Menezes Salgado Luiz Alberto Beijo

Citations