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Metamodeling methods that incorporate qualitative variables for improved design of vegetative filter strips

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  • Lauvernet, Claire
  • Helbert, Céline

Abstract

Significant amounts of pollutant are measured in surface water, their presence due in part to the use of pesticides in agriculture. One solution to limit pesticide transfer by surface runoff is to implement vegetative filter strips. The sizing of VFSs is a major issue, with influencing factors that include local conditions (climate, soil, vegetation). The BUVARD modeling toolkit was developed to design VFSs throughout France according to these properties. This toolkit includes the numerical model VFSMOD, which quantifies dynamic effects of VFS on site-specific pesticide mitigation efficiency. In this paper, a metamodeling, or model dimension reduction, approach is proposed to ease the use of BUVARD and to help users design VFSs that are adapted to specific contexts. Three different reduced models, or surrogates, are compared: a linear model, GAM, and kriging. It is shown that kriging, implemented with a covariance kernel for a mixture of qualitative and quantitative inputs, outperforms the other metamodels. The metamodel is a way of providing a relevant first approximation to help design the pollution reduction device. In addition, it is a relevant tool to visualize the impact that lack of knowledge of some field parameters can have when performing pollution risk analysis and management.

Suggested Citation

  • Lauvernet, Claire & Helbert, Céline, 2020. "Metamodeling methods that incorporate qualitative variables for improved design of vegetative filter strips," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020305846
    DOI: 10.1016/j.ress.2020.107083
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    References listed on IDEAS

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    1. Dupuy, Delphine & Helbert, Céline & Franco, Jessica, 2015. "DiceDesign and DiceEval: Two R Packages for Design and Analysis of Computer Experiments," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i11).
    2. Janis Janusevskis & Rodolphe Le Riche, 2013. "Simultaneous kriging-based estimation and optimization of mean response," Journal of Global Optimization, Springer, vol. 55(2), pages 313-336, February.
    3. Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).
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