Year of Award
2019
Document Type
Thesis
Degree Type
Master of Science (MS)
Degree Name
Computer Science
Department or School/College
Computer Science
Committee Chair
Jesse Johnson
Commitee Members
Marco Maneta, Douglas Brinkerhoff
Keywords
Kalman Filter, HIERARCHICAL, Machine Learning, Computer, Hydrology
Subject Categories
Artificial Intelligence and Robotics | Hydrology | Other Mathematics
Abstract
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.
Recommended Citation
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.
https://scholarworks.umt.edu/etd/11489
Included in
Artificial Intelligence and Robotics Commons, Hydrology Commons, Other Mathematics Commons
© Copyright 2019 William J. Cook, Jesse Johnson, Marko Maneta, and Doug Brinkerhoff