|M.Sc Student||Salomons Shani|
|Subject||Development of a Calibration Model for CE-QUAL-W2|
|Department||Department of Civil and Environmental Engineering||Supervisor||Professor Avi Ostfeld|
This work 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 three example applications: a synthetic hypothetical example calibrated for temperature, serving for tuning the GA-kNN parameters; the Lake Kinneret case study in Israel calibrated for temperature; 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.