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Peer-reviewed Publications |
2009 |
Zou R, Lung WS, Wu J (2009) Multiple-pattern parameter identification and uncertainty analysis approach for water quality modeling. Ecol. Model. 220(5):621–629
Abstract: 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. The procedure was demonstrated through a coupled 2D hydrodynamic and eutrophication model for Loch Raven Reservoir in Maryland. The inverse problem was formulated as a nonlinear optimization problem minimizing the degree of misfit (DOM) between model results and observed data. A set of NN models was developed to approximate the input-output response relationship of the Loch Raven Reservoir model and was incorporated into a GA framework in an adaptive fashion to search for near-optimal solutions minimizing the DOM. The numerical example showed that the adaptive NN-GA approach is capable of identifying multiple parameter patterns that reproduce the observed data equally well. The resulting parameter patterns were incorporated into the numerical model, and a multiple-pattern robust water quality modeling analysis, along with a compound margin of safety (CMOS) method, was proposed and applied to analyze the parameter pattern uncertainty. (C) 2008 Elsevier B.V. All rights reserved.
Keywords: Multiple-pattern parameter identification; Uncertainty analysis; Water quality modeling; Neural network; Genetic algorithms
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2007 |
Zou R, Lung WS, Wu J (2007) An adaptive neural network embedded genetic algorithm approach for inverse water quality modeling. Water Resour. Res. 43(8):13 pp
Abstract: 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. As a remedy, an adaptive NN-GA approach is proposed to achieve a gradual convergence toward the near optima through an iterative network learning method. The proposed approach is applied to a full-scale, numerical example, and the result shows that the adaptive NN-GA approach is capable of obtaining near-optimal solutions for the inverse problem of a complicated water quality model.
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2006 |
Wu J, Yu SL, Zou R (2006) A water quality-based approach for watershed wide BMP strategies. J. Am. Water Resour. Assoc. 42(5):1193–1204
Abstract: 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. To address these issues, this paper proposes a water quality-based BMP planning approach for effective nonpoint source pollution control at a watershed scale. A coupled modeling system consisting of a watershed model (HSPF) and a receiving water quality model (CE-QUAL-W2) was developed to establish the linkage between BMP performance and receiving water quality targets. A Monte Carlo simulation approach was utilized to develop alternative BMP strategies at a watershed level. The developed methodology was applied to the Swift Creek Reservoir watershed in Virginia, and the results show that the proposed approach allows for the development of BMP strategies that lead to full compliance with water quality requirements.
Keywords: BMP; nonpoint source pollution; watershed management; nutrient management; watershed modeling; Monte Carlo simulation
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Wu J, Zou R, Yu SL (2006) Uncertainty analysis for coupled watershed and water quality modeling systems. J. Water Resour. Plan. Manage.-ASCE 132(5):351–361
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 processes. First-order error analysis showed that among a large number of model parameters, only a few significantly affected the variation in pollutant loads at the watershed outlets and concentrations in the receiving body of water, and the variation in pollutant concentrations is greater than the variation in pollutant loads. The uncertainty analysis regarding the loads and concentrations showed different patterns, underscoring the importance of a complete uncertainty analysis and the need for an explicit quantification of the errors associated with the predicted loads. Monte Carlo simulation showed that best management practice scenarios considered as a safe scheme based on a deterministic model could actually lead to a significant risk of violating the water quality standards when model uncertainty is considered. With a modeling framework that considers uncertainty, feasible alternatives can be evaluated and ranked based on their risks of exceeding the target water quality criteria.
Keywords: errors; simulation; watershed management; water quality; nonpoint pollution
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