Robust Predictive Design of Field Measurements for Evapotranspiration Barriers Using Universal Multiple linear Regression

Melissa Clutter, Ty P.A. Ferré, Zhuanfang Fred Zhang, Hoshin Gupta

Research output: Contribution to journalArticle

Abstract

Surface barriers are commonly installed to reduce downward water movement into contaminated zones. Specifically, evapotranspiration (ET) barriers are used to store water and release it, via ET, before it can percolate into an underlying waste zone. To assess the effectiveness of a surface barrier, we used an existing data set, model-simulated data, and a dimensionality reduction approach called universal multiple linear regression (uMLR) to optimize the required number of sensors in a 2-m thick surface barrier. To understand the usefulness of implementing predictive uMLR to accommodate multiple monitoring objectives, we compare several network designs, selected based on down-sampling of existing data, with a recommended sensor design based on model simulations performed without consideration of existing data. We also added consideration of “fuzzy” design, which allows more practical guidelines for field implementation of uMLR. We found that uMLR, combined with robust decision-making, provides a simple, flexible, and high-quality network design for monitoring the total water stored in a surface barrier across multiple uncertain conditions.

Original languageEnglish (US)
Pages (from-to)8478-8491
Number of pages14
JournalWater Resources Research
Volume55
Issue number11
DOIs
StatePublished - Nov 1 2019

Keywords

  • linear regression
  • measurement
  • network design
  • observation
  • optimization

ASJC Scopus subject areas

  • Water Science and Technology

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