Stand Basal Area Growth Models for 4 Main Coniferous Species in Kunming

Algorithms of levenberg-marquardt, differential evolution, genetic algorithm, simulated annealing, and particle swarm optimization were applied for parameters solving of stand basal area growth model for Richards, Schumacher and Korf. Determination coefficient and root mean square error were used for selecting suitable parameters. Iterations were used for comparisons of different algorithms efficiency. Residuals were used for parameters stability analysis. Results show that falling point test of site class model are 97.9%, 98.3%, 98.1%, and 98.9% for Pinus armadii, Pinus yunnanensis, Keteleeria fortune, Cupressus funebris, respectively. Site class tables could be used in forest management. Parameter solving efficiency from high to low is LM > DE > PSO > GA > SA, Goodness of fitting applying PSO is poor; Richards model is more suitable for the growth model of conifer species, Parameter fitting results of Schumacher model are more stable.