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    Designing Robust Measurement Networks Using Universal Multiple Linear Regression

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    Author
    Clutter, Melissa Jean
    Issue Date
    2019
    Keywords
    design
    linear regression
    measurement
    optimization
    Advisor
    Ferre, Ty
    
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    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Collecting hydrological data is essential for understanding system behavior and processes; without it, there is no basis for predictive modeling or risk assessment. Unfortunately, limited monitoring budgets often restrict measurement designs for field-based studies. Therefore, most field studies require some sort of data worth analysis to identify the most important data to collect with respect to the prediction(s) of interest. Data worth analyses can be either informal using methods such as trial-and-error, intuition, or rules of thumb, or formal using a quantitative metric to identify the most valuable data. My research focuses on a simple, computationally inexpensive formal data worth analysis which can be used in conjunction with more complex optimization approaches or when they are not warranted. A key to network design is that the selection of sensor type, timing, and placement should be both informative and efficient. There are many possible individual sensor types and installation depths, and the key is to determine which sets of observations would be most effective prior to data collection. My research explores a combination of a method called universal Multiple Linear Regression (uMLR) and Robust Decision Making (RDM) to identify these best observation sets. The uMLR method quantifies the explanatory power of all possible combinations of observations to the prediction(s) of interest and the RDM strategy further explores the impacts of user-defined uncertainties, including measurement error and parameter uncertainty, on these observation-set selections. Robust Decision Making is a concept developed by the Research and Development (RAND) Corporation and is designed to select a robust outcome under a range of uncertainty, at the risk of the selection being sub-optimal for any one specific uncertain outcome. Norgaard et al., (2014) previously used the uMLR approach to downsampling pre-existing data to identify a reduced set of parameters to describe the dispersibility of colloids. I offer an extension of the uMLR downsampling approach, based on model-simulated data, to consider optimizing data that have not yet been collected.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Hydrology
    Degree Grantor
    University of Arizona
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