A hybrid genetic—instance based learning algorithm for CE-QUAL-W2 calibration

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 relation...

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Vydáno v:	Journal of hydrology (Amsterdam) Ročník 310; číslo 1; s. 122 - 142
Hlavní autoři:	Ostfeld, Avi, Salomons, Shani
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.08.2005 Elsevier Science
Témata:	algae algae and seaweeds algorithms Calibration CE-QUAL-W2 CE-QUAL-W2 model dissolved oxygen Earth sciences Earth, ocean, space eutrophication Exact sciences and technology floodplains Genetic algorithm genetic algorithms Geochemistry hydrologic models Hydrology Hydrology. Hydrogeology Instance based learning Lower Columbia Slough Mineralogy nutrients Oregon organic matter sediment deposition Silicates Simulation surface water Water geochemistry Water quality water temperature Oregon United States Instance based learning Calibration Simulation CE-QUAL-W2 Genetic algorithm Water quality algorithms models sensitivity analysis eutrophication Plantae algae hydrodynamics digital simulation case studies North America nutrients surface water hydrochemistry water quality organic materials temperature two-dimensional models calibration oxygen Thallophyta
ISSN:	0022-1694, 1879-2707
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	0022-1694 1879-2707
DOI:	10.1016/j.jhydrol.2004.12.004