A tool for efficient, model-independent management optimization under uncertainty
| Autor: | Dave E. Welter, Paul M. Barlow, Michael N. Fienen, Jeremy T. White |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
Environmental Engineering Linear programming Computer science Interface (Java) Ecological Modeling Probabilistic-based design optimization 0208 environmental biotechnology 02 engineering and technology Function (mathematics) 020801 environmental engineering Engineering optimization Constraint (information theory) Resource management Groundwater model Software |
| Zdroj: | Environmental Modelling & Software. 100:213-221 |
| ISSN: | 1364-8152 |
| DOI: | 10.1016/j.envsoft.2017.11.019 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect