A tool for efficient, model-independent management optimization under uncertainty
Links
- More information: Publisher Index Page (via DOI)
- Download citation as: RIS | Dublin Core
Abstract
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.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | A tool for efficient, model-independent management optimization under uncertainty |
| Series title | Environmental Modelling and Software |
| DOI | 10.1016/j.envsoft.2017.11.019 |
| Volume | 100 |
| Year Published | 2018 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | Wisconsin Water Science Center |
| Description | 9 p. |
| First page | 213 |
| Last page | 221 |
| Google Analytic Metrics | Metrics page |