Machine learning methods for empirical streamflow simulation: A comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds

Hydrology and Earth System Sciences

Volumn 20, Issue 7, 2016, Pages 2611-2628

  Shortridge, Julie E ^a   Guikema, Seth D ^b   Zaitchik, Benjamin F ^c  

^a The Johns Hopkins University   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

^c JOHNS HOPKINS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION TREES; LEARNING SYSTEMS; NEURAL NETWORKS; REGRESSION ANALYSIS; SPLINES; STREAM FLOW;

GENERALIZED ADDITIVE MODEL; INTERPRETATION OF MODELS; MACHINE LEARNING APPROACHES; MACHINE LEARNING METHODS; MULTIVARIATE ADAPTIVE REGRESSION SPLINES; NON-PARAMETRIC REGRESSION; PREDICTIVE PERFORMANCE; STREAMFLOW SIMULATIONS;

CLIMATE MODELS;

ACCURACY ASSESSMENT; CLIMATE CONDITIONS; COMPARATIVE STUDY; DATA INTERPRETATION; EMPIRICAL ANALYSIS; HYDROLOGICAL MODELING; MACHINE LEARNING; MULTIVARIATE ANALYSIS; PREDICTION; SEASONALITY; STREAMFLOW; UNCERTAINTY ANALYSIS; VISUALIZATION; WATERSHED;

ETHIOPIAN HIGHLANDS;

EID: 84978162106     PISSN: 10275606     EISSN: 16077938     Source Type: Journal    
DOI: 10.5194/hess-20-2611-2016     Document Type: Article

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