Spatio-temporal additive regression model selection for urban water demand

Hunter R. Merrill, Xueying Tang, Nikolay Bliznyuk

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Understanding the factors influencing urban water use is critical for meeting demand and conserving resources. To analyze the relationships between urban household-level water demand and potential drivers, we develop a method for Bayesian variable selection in partially linear additive regression models, particularly suited for high-dimensional spatio-temporally dependent data. Our approach combines a spike-and-slab prior distribution with a modified version of the Bayesian group lasso to simultaneously perform selection of null, linear, and nonlinear models and to penalize regression splines to prevent overfitting. We investigate the effectiveness of the proposed method through a simulation study and provide comparisons with existing methods. We illustrate the methodology on a case study to estimate and quantify uncertainty of the associations between several environmental and demographic predictors and spatio-temporally varying household-level urban water demand in Tampa, FL.

Original languageEnglish (US)
Pages (from-to)1075-1087
Number of pages13
JournalStochastic Environmental Research and Risk Assessment
Issue number4-6
StatePublished - Jun 14 2019
Externally publishedYes


  • Bayesian group lasso
  • Geoadditive model
  • High dimensional data
  • Sparsity

ASJC Scopus subject areas

  • Water Science and Technology
  • Safety, Risk, Reliability and Quality
  • General Environmental Science
  • Environmental Engineering
  • Environmental Chemistry


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