A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network

Journal of Hydrology

Volumn 540, Issue , 2016, Pages 623-640

  Humphrey, Greer B ^a   Gibbs, Matthew S ^a,b   Dandy, Graeme C ^a   Maier, Holger R ^a  

^a UNIVERSITY OF ADELAIDE   (Australia)

^b Water and Natural Resources ^*  (Australia)

Author keywords

Bayesian artificial neural networks; Conceptual hydrological models; Hybrid modelling; Monthly streamflow forecasting; South Australia; Uncertainty

Indexed keywords

BAYESIAN NETWORKS; CATCHMENTS; DECISION MAKING; GROUNDWATER; GROUNDWATER RESOURCES; HYDROLOGY; MOISTURE; NEURAL NETWORKS; RUNOFF; SOIL MOISTURE; STREAM FLOW; UNCERTAINTY ANALYSIS; WATER RESOURCES;

BAYESIAN ARTIFICIAL NEURAL NETWORKS; HYBRID MODELLING; HYDROLOGICAL MODELS; SOUTH AUSTRALIA; STREAMFLOW FORECASTING; UNCERTAINTY;

FORECASTING;

ARTIFICIAL NEURAL NETWORK; BASEFLOW; BAYESIAN ANALYSIS; CATCHMENT; DECISION MAKING; HYDROLOGICAL MODELING; QUANTITATIVE ANALYSIS; RUNOFF; SOIL MOISTURE; UNCERTAINTY ANALYSIS;

AUSTRALIA; SOUTH AUSTRALIA;

EID: 84976648644     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2016.06.026     Document Type: Article

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