A tool for efficient, model-independent management optimization under uncertainty
To fill a need for risk-based environmental management optimization, we have developed PESTPP-OPT, a model-independent tool for resource management optimization under uncertainty. PESTPP-OPT solves a sequential linear programming (SLP) problem and also implements (optional) efficient, “on-the-fly” (without user intervention) first-order, second-moment (FOSM) uncertainty techniques to estimate model-derived constraint uncertainty. Combined with a user-specified risk value, the constraint uncertainty estimates are used to form chance-constraints for the SLP solution process, so that any optimal solution includes contributions from model input and observation uncertainty. In this way, a “single answer” that includes uncertainty is yielded from the modeling analysis. PESTPP-OPT uses the familiar PEST/PEST++ model interface protocols, which makes it widely applicable to many modeling analyses. The use of PESTPP-OPT is demonstrated with a synthetic, integrated surface-water/groundwater model. The function and implications of chance constraints for this synthetic model are discussed.
Citation Information
Publication Year | 2018 |
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Title | A tool for efficient, model-independent management optimization under uncertainty |
DOI | 10.1016/j.envsoft.2017.11.019 |
Authors | Jeremy T. White, Michael N. Fienen, Paul M. Barlow, Dave E. Welter |
Publication Type | Article |
Publication Subtype | Journal Article |
Series Title | Environmental Modelling and Software |
Index ID | 70196067 |
Record Source | USGS Publications Warehouse |
USGS Organization | Wisconsin Water Science Center |