Abstract
Before we build a predictive groundwater flow model, we first identify candidate conceptual models. To cull our collection of candidate models, we ask the seemingly simple but key question: Where is the water coming from? We argue for the use of a Bayesian framework to address this question. We start with an uninformed prior distribution that says all directions are equally likely. We then incorporate the available information (usually error-prone, noisy information of varied quality) and appropriately update the characterization of the uncertain direction. When the added information is extensive and internally consistent, a clear flow direction emerges. On the other hand, if the added information is minimal or internally inconsistent, the uninformed prior is only slightly modified.
Original language | English (US) |
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Pages (from-to) | 78-86 |
Number of pages | 9 |
Journal | Geotechnical Special Publication |
Volume | 2020-February |
Issue number | GSP 321 |
DOIs | |
State | Published - 2020 |
Externally published | Yes |
Event | Geo-Congress 2020: University of Minnesota 68th Annual Geotechnical Engineering Conference - Minneapolis, United States Duration: Feb 25 2020 → Feb 28 2020 |
Bibliographical note
Publisher Copyright:© 2020 American Society of Civil Engineers.