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 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.