Abstract
Optimal management of water resources systems has been addressed mostly by
deterministic models that assume perfectly known hydrological data. These models
yield decisions which may perform poorly when implemented in the real world, as
the problem data are revealed and differ from those assumed in the model. More
sophisticated optimisation models that are stochastic in nature have been studied, but most of them require assumptions about the Probability Density Function (PDF) of the uncertain hydrological variables and their dependencies based on an historical sample. This paper opens with a brief review of a number of models that use this approach.
deterministic models that assume perfectly known hydrological data. These models
yield decisions which may perform poorly when implemented in the real world, as
the problem data are revealed and differ from those assumed in the model. More
sophisticated optimisation models that are stochastic in nature have been studied, but most of them require assumptions about the Probability Density Function (PDF) of the uncertain hydrological variables and their dependencies based on an historical sample. This paper opens with a brief review of a number of models that use this approach.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Dooge Nash International Symposium |
| Pages | 355-364 |
| State | Published - 2014 |