Litcius/Paper detail

Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling

Debaditya Chakraborty, Hakan Başağaoğlu, James Winterle

2020Expert Systems with Applications138 citationsDOI

Topics & Concepts

Computer scienceMachine learningMissing dataEvapotranspirationDecision treeArtificial intelligenceEnsemble forecastingTree (set theory)Ensemble learningRandom forestSupport vector machineData miningMathematicsBiologyMathematical analysisEcologyHydrological Forecasting Using AIHydrology and Watershed Management StudiesGroundwater flow and contamination studies
Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling | Litcius