Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

Hydrology and Earth System Sciences

Volumn 17, Issue 7, 2013, Pages 2669-2684

  Galelli, S ^a   Castelletti, A ^b,c  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b Dipartimento di Elettronica   (Italy)

^c UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL REQUIREMENTS; COMPUTATIONALLY EFFICIENT; DATA-DRIVEN APPROACH; DATA-DRIVEN MODELLING; MULTIPLE LINEAR REGRESSIONS; PHYSICAL INTERPRETATION; PREDICTION CAPABILITY; PREDICTIVE CAPABILITIES;

CATCHMENTS; COMPUTATIONAL EFFICIENCY; DECISION TREES; LINEAR REGRESSION; STREAM FLOW;

TREES (MATHEMATICS);

ASSESSMENT METHOD; COMPUTER SIMULATION; ENSEMBLE FORECASTING; HYDROLOGICAL MODELING; NUMERICAL MODEL; PREDICTION; STREAMFLOW; WATERSHED; DATA SET;

AUSTRALIA; CANNING RIVER [WESTERN AUSTRALIA]; SINGAPORE [SOUTHEAST ASIA]; WESTERN AUSTRALIA;

EID: 84880153684     PISSN: 10275606     EISSN: 16077938     Source Type: Journal    
DOI: 10.5194/hess-17-2669-2013     Document Type: Article

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