Background: In radioactive nuclides atmospheric diffusion models

the empirical dispersion coefficients were deduced under certain experiment conditions

whose difference with nuclear accident conditions is a source of deviation. A better estimation of the radioactive nuclide’s actual dispersion process could be done by correcting dispersion coefficients with observation data

and Genetic Algorithm (GA) is an appropriate method for this correction procedure. Purpose: This study is to analyze the fitness functions’ influence on the correction procedure and the forecast ability of diffusion model. Methods: GA

coupled with Lagrange dispersion model

was used in a numerical simulation to compare 4 fitness functions’ impact on the correction result. Results: In the numerical simulation

the fitness function with observation deviation taken into consideration stands out when significant deviation exists in the observed data. After performing the correction procedure on the Kincaid experiment data

a significant boost was observed in the diffusion model’s forecast ability. Conclusion: As the result shows

in order to improve dispersion models’ forecast ability using GA

observation data should be given different weight in the fitness function corresponding to their error.Cited