Year of Award
Master of Science (MS)
Department or School/College
Marco Maneta, Douglas Brinkerhoff
Kalman Filter, HIERARCHICAL, Machine Learning, Computer, Hydrology
University of Montana
Artificial Intelligence and Robotics | Hydrology | Other Mathematics
Dynamic models that simulate processes across large geographic locations, such as hydrologic models, are often informed by empirical parameters that are distributed across a geographical area and segmented by geological features such as watersheds. These parameters may be referred to as spatially distributed parameters. Spatially distributed parameters are frequently spatially correlated and any techniques utilized in their calibration ideally incorporate existing spatial hierarchical relationships into their structure. In this paper, a parameter estimation method based on the Dual State Ensemble Kalman Filter called the Dual State Hierarchical Ensemble Kalman Filter (DSHEnKF) is presented. This modified filter is innovative in that it allows parameters to be placed into a set of groups that are smoothed using hierarchical modeling techniques. The usability and effectiveness of this new technique is demonstrated by applying it to a rainfall-runoff model that simulates subcatchment-scale hydrologic processes and contains high dimensional spatially distributed empirical parameters.
Cook, William J.; Johnson, Jesse; Maneta, Marko; and Brinkerhoff, Doug, "A DUAL STATE HIERARCHICAL ENSEMBLE KALMAN FILTER ALGORITHM" (2019). Graduate Student Theses, Dissertations, & Professional Papers. 11489.
© Copyright 2019 William J. Cook, Jesse Johnson, Marko Maneta, and Doug Brinkerhoff