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Application of genetic algorithms to calibration and verification of QUAL2E

Göktaş, Recep Kaya
The objective of this study is to develop a calibration and verification tool for the QUAL2E Model by using Genetic Algorithms. In the developed optimization model, an objective function that is formulated on the basis of the sum-of-least squares approach aiming at minimizing the difference between the observed and simulated quantities was used. In order to perform simultaneous calibration and verification, verification of the calibrated results was treated as a constraint and inserted into the objective function as a penalty function. The performance of the optimization model was tested for different observation data qualities represented by the synthetic perfect and biased data sets. Although it was not possible to obtain the exact values of the kinetic coefficients for any of the tests performed, the coefficient estimates were successful in reflecting the water quality variable profiles in the river. The results of the tests showed that the performance of the optimization model is generally sensitive to the error in the observed data sets, to the number and location of sampling points, and to the objective function formulation. For the problems that involve multiple water quality variables, a weighting approach used in the objective function formulation resulted in better performances. The optimization model was also applied for a case study. For the same input data, calibration obtained with the genetic algorithm optimization ا simulation was better compared to the trial-and-error approach.