A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA

Journal of Hydrology

Volumn 531, Issue , 2015, Pages 902-911

  Nolan, Bernard T ^a   Fienen, Michael N ^b   Lorenz, David L ^a  

^a U S GEOLOGICAL SURVEY   (United States)

^b WISCONSIN WATER SCIENCE CENTER   (United States)

Author keywords

Artificial neural networks; Bayesian networks; Boosted regression trees; Cross validation; Groundwater; Nitrate

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; COMPLEX NETWORKS; DECISION TREES; FORECASTING; FORESTRY; GROUNDWATER; LANDFORMS; LEARNING SYSTEMS; LINEAR REGRESSION; NEURAL NETWORKS; NITRATES; REGRESSION ANALYSIS;

BOOSTED REGRESSION TREES; CROSS VALIDATION; MACHINE LEARNING METHODS; MULTIPLE LINEAR REGRESSIONS; NITRATE CONCENTRATION; PREDICTION PERFORMANCE; PREDICTIVE PERFORMANCE; THREE MACHINE LEARNING METHODS;

STATISTICAL TESTS;

ARTIFICIAL NEURAL NETWORK; BAYESIAN ANALYSIS; CONCENTRATION (COMPOSITION); DATA SET; GROUNDWATER; HYDROLOGICAL MODELING; MODEL VALIDATION; NITRATE; OPTIMIZATION; PERFORMANCE ASSESSMENT; REGRESSION ANALYSIS; SHALLOW WATER; SPATIOTEMPORAL ANALYSIS;

CALIFORNIA; CENTRAL VALLEY [CALIFORNIA]; UNITED STATES;

EID: 84948173143     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2015.10.025     Document Type: Article

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