Abstract:

In this paper, forecast combination was introduced to improve stand basal area prediction accuracy and compatibility. A linear combination of two or more predictions may often yield more accurate forecasts than using a single model to the extent that the component forecasts contain useful and independent information. But it is very important to calculate weight coefficients for improving forecast combination. Based on the data of the Chinese pine in Beijing mountains, sum of squared errors method, variance-covariance method and optimal weight method were used to calculate weight coefficients of combined model. Results show that forecast combination for predicting stand basal area outperformed over the stand-level and tree-level models respectively, and meanwhile, stand basal area model based on the optimal weight method(R²=0.929 2) is superior to other two methods, and variance-covariance method(R²=0.929 1) is better than the sum of squared errors method(SSE)(R²=0.929 1).

Key words: stand basal area, forecast combination, sum of squared errors method(SSE), variance-covariance method, optimal weight method