Genetic Programming for Prediction of Water Flow and Transport of Solids in a Basin

Rabuñal, Juan R.; Puertas, Jerónimo; Rivero, Daniel; Fraga, Ignacio; Cea, Luís; Garrido, Marta

doi:10.1007/978-3-642-21326-7_25

Search citation statements

Order By: Relevance

Paper Sections

Select...

1029/2020wr0283921

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2013

2021

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The latter are artificial intelligence data-driven methods. More specifically, this category includes machine learning techniques, including the commonly used artificial neural networks (Coulibaly et al, 1999;Maier & Dandy, 2000;Maier et al, 2010; ∼65% of the referenced studies), fuzzy inference systems (Özger, 2009), evolutionary computation with genetic programming (Charhat et al, 2009) and other techniques, such as wavelet-based artificial intelligence techniques, hidden Markov chain models and chaos theory (Cui & Singh, 2015;Fortin et al, 2004;Liu et al, 2014).…”

Section: 1029/2020wr028392mentioning

confidence: 99%

Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years

Troin

Arsenault

Wood

et al. 2021

Water Resources Research

View full text Add to dashboard Cite

 This work summarizes the 40 years of research in the generation of streamflow forecasts based on an exhaustive review of studies.  Ensemble prediction systems are categorized into three classes: statistics-based, climatology-based and numerical weather prediction-based hydrological ensemble prediction systems.  For each ensemble forecasting system, thorough technical information is provided.  Findings from the success of the model-based ensemble forecasting approaches are analysed, as are prospective future developments.

show abstract