A novel method to estimate model uncertainty using machine learning techniques

Water Resources Research

Volumn 45, Issue 1, 2009, Pages

  Solomatine, Dimitri P ^a,b   Shrestha, Durga Lal ^a  

^a UNESCO IHE INSTITUTE FOR WATER EDUCATION   (Netherlands)

^b DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ERROR DISTRIBUTIONS; ESTIMATE MODEL; HYDROLOGICAL CONDITION; HYDROLOGICAL MODELS; IMPROVED MODELS; M5 MODEL TREE; MACHINE LEARNING TECHNIQUES; MODEL ERRORS; MODEL UNCERTAINTIES; NEW RESULTS; PARAMETERS CHARACTERIZING; RAINFALL-RUNOFF MODELING; RAINFALL-RUNOFF MODELS; TARGET VALUES; TRAINING SETS; UNCERTAINTY ESTIMATION; HYDROLOGICAL MODELING; MACHINE LEARNING MODELS;

CATCHMENTS; ESTIMATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; PROBABILITY DISTRIBUTIONS; RAIN; RUNOFF;

UNCERTAINTY ANALYSIS;

EID: 72149087074     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2008WR006839     Document Type: Article

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