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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Veröffentlicht in:Journal of hydrology (Amsterdam) Jg. 310; H. 1; S. 122 - 142
Hauptverfasser: Ostfeld, Avi, Salomons, Shani
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.08.2005
Elsevier Science
Schlagworte:
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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Zusammenfassung: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.
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ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2004.12.004