Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models

Use of Nested Flow Models and Interpolation Techniques for Science-Based Management of the Sheyenne National Grassland, North Dakota, USA

(2012) M.A. Gusyev et al.   Groundwater

Dynamic modeling of predictive uncertainty by regression on absolute errors

(2012) F. Pianosi et al.   WATER RESOURCES RESEARCH

Daily streamflow forecasting by machine learning methods with weather and climate inputs

(2011) Kabir Rasouli et al.   JOURNAL OF HYDROLOGY

Use of paired simple and complex models to reduce predictive bias and quantify uncertainty

(2011) John Doherty et al.   WATER RESOURCES RESEARCH

Taking Account of “Unknown Unknowns”

(2010) Randall J. Hunt et al.   Groundwater

A short exploration of structural noise

(2010) John Doherty et al.   WATER RESOURCES RESEARCH

Integrating a calibrated groundwater flow model with error-correcting data-driven models to improve predictions

(2008) Yonas K. Demissie et al.   JOURNAL OF HYDROLOGY

Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach

(2008) T.M.K.G. Fernando et al.   JOURNAL OF HYDROLOGY

On model selection criteria in multimodel analysis

(2008) Ming Ye et al.   WATER RESOURCES RESEARCH

Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation

(2008) Jasper A. Vrugt et al.   WATER RESOURCES RESEARCH