Abstract: Presented herein is a robust approach to calibrating water quality models for water quality management using sparse field data. The calibration procedure adopts genetic algorithms (GAs) to inversely solve the governing ...

Abstract: This paper proposes a neural network embedded Monte Carlo (NNMC) approach to account for uncertainty in water quality modeling. The framework of the proposed method has three major parts: a numerical water quality model, ...

Abstract: Water quality management is subject to large uncertainties due to inherent randomness in the natural system and vagueness in the decision-making process. For water quality management optimization models, this means that ...

Abstract: A series of uncertainty analysis methods was applied to investigate the propagation of parameter uncertainty within a coupled model system and to evaluate the effects of uncertainty on model outputs and decision-making ...

Abstract: This paper presents a hydrodynamic and water quality modeling system for Wissahickon Creek, Pa. Past data show that high nutrient levels in Wissahickon Creek were linked to large diurnal fluctuations in oxygen concentration, ...

Abstract: The civil and environmental decision-making processes are plagued with uncertain, vague, and incomplete information. Interval linear programming (ILP) is a widely applied mathematical programming method in assisting civil ...