Litcius/Paper detail

Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models

Moges B. Wagena, D. Goering, Amy S. Collick, Emily Bock, Daniel R. Fuka, Anthony R. Buda, Zachary M. Easton

2020Environmental Modelling & Software111 citationsDOIOpen Access PDF

Topics & Concepts

StreamflowQuantitative precipitation forecastArtificial neural networkFlood forecastingBayesian probabilityPrecipitationAutoregressive modelEnvironmental scienceEnsemble forecastingTerm (time)Soil and Water Assessment ToolComputer scienceMeteorologyEconometricsMathematicsMachine learningArtificial intelligenceDrainage basinGeographyQuantum mechanicsPhysicsCartographyHydrological Forecasting Using AIHydrology and Watershed Management StudiesFlood Risk Assessment and Management
Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models | Litcius