期刊
JOURNAL OF HYDROLOGY
卷 310, 期 1-4, 页码 122-142出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jhydrol.2004.12.004
关键词
calibration; CE-QUAL-W2; genetic algorithm; instance based learning; water quality; simulation
This paper presents a calibration model for CE-QUAL-W2. CE-QUAL-W2 is a two-dimensional (2D) longitudinal/vertical hydrodynamic and water quality model for surface water bodies, modeling eutrophication processes such as temperature-nutrient-algae-dissolved oxygen-organic matter and sediment relationships. The proposed methodology is a combination of a 'hurdle-race' and a hybrid Genetic-k-Nearest Neighbor algorithm (GA-kNN). The 'hurdle race' is formulated for accepting-rejecting a proposed set of parameters during a CE-QUAL-W2 simulation; the k-Nearest Neighbor algorithm (kNN)-for approximating the objective function response surface; and the Genetic Algorithm (GA)-for linking both. The proposed methodology overcomes the high, non-applicable, computational efforts required if a conventional calibration search technique was used, while retaining the quality of the final calibration results. Base runs and sensitivity analysis are demonstrated on two example applications: a synthetic hypothetical example calibrated for temperature, serving for tuning the GA-kNN parameters; and the Lower Columbia Slough case study in Oregon US calibrated for temperature and dissolved oxygen. The GA-kNN algorithm was found to be robust and reliable, producing similar results to those of a pure GA, while reducing running times and computational efforts significantly, and adding additional insights and flexibilities to the calibration process. (C) 2005 Elsevier B.V. All rights reserved.
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