STOCHASTIC DYNAMIC-PROGRAMMING MODELS FOR WATER-QUALITY MANAGEMENT

This paper presents optimization models for waste load allocation from multiple point sources which include both parameter (Type II) and model (Type I) uncertainty. These optimization models employ more sophisticated water quality simulation models, for example, in the case of dissolved oxygen modeling, QUAL2E and WASP4, than is typically the norm in studies on the optimization of waste load allocation. Variability in selected input parameters to the water quality simulation models gives rise to stochastic dynamic programming approaches. Two types of reliability and feasibility attributes are highlighted, associated with the management options that are generated. Several dissolved oxygen simulation models are incorporated into the optimization procedures to explore the effects of Type I uncertainty on control decisions. Information from simultaneous consideration of multiple simulation models is aggregated in the dynamic programming framework through two regret-based formulations. By accommodating both model and parameter uncertainty in the modeling framework, trade-offs can be generated between the two so as to assess their influence on control decisions. The models are applied to a waste load allocation problem for the Schuylkill River in Pennsylvania.