This paper presents a multiple-pattern parameter identification and uncertainty analysis approach for robust water quality modeling using a neural network (NN) embedded genetic algorithm (GA). The modeling approach uses an adaptive NN-GA framework to inversely solve the governing equations in a water quality model for multiple parameter patterns. along with an alternating fitness method to maintain solution diversity.

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This paper proposes a neural network (NN)-embedded genetic algorithm (GA) approach for solving inverse water quality modeling problems to overcome the computational bottleneck of inverse modeling by replacing a water quality model with an efficient NN functional evaluator. An existing one-step, NN-embedded GA approach is found incapable of solving an inverse water quality modeling problem because it tends to fail in guiding the global search process to converge toward the near optima.

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Watershed management strategies generally involve controlling nonpoint source pollution by implementing various best management practices (BMPs). Currently, stormwater management programs in most states use a performance-based approach to implement onsite BMPs. This approach fails to link the onsite BMP performance directly to receiving water quality benefits, and it does not take into account the combined treatment effects of all the stormwater management practices within a watershed.

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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 processes.

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