This study proposes a new parameter estimation approach for the mixture normal distribution. The developed model estimates parameters of the mixture normal distribution by maximizing the log likelihood function using a meta-heuristic algorithm-genetic algorithm (GA). To verify the performance of the developed model, simulation experiments and practical applications are implemented. From the results of experiments and practical applications, the developed model presents some advantages, such as (1) the proposed model more accurately estimates the parameters even with small sample sizes compared to the expectation maximization (EM) algorithm; (2) not diverging in all application; and (3) showing smaller root mean squared error and larger log likelihood than those of the EM algorithm. We conclude that the proposed model is a good alternative in estimating the parameters of the mixture normal distribution for kutotic and bimodal hydrometeorological data.

• Taesam Lee(Department of Civil Engineering Gyeongsang National University) Corresponding author
• Juyoung Shin(Masdar Institute of Science and Technology)
• Junhaeng Heo(Department of Civil and Environmental Engineering, Yonsei University)
• Changsam Jeong(Department of Civil and Environmental Engineering, Induk University)