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In conclusion, the OOB error for the model prediction precision was not sensitive to the number of trees.
The purpose of the BASE model in the current study was to provide a baseline for evaluating the effect of including an upper stem diameter measurement, in the DH+ models GB99 and M3, on taper model prediction precision and bias.
We estimated the model prediction precision by taking the root mean square prediction error (RMSPE).
We estimated the model prediction precision by taking the square root of the mean squared prediction errors (RMSPE) (Yanosky et al. 2008).
In the table, model prediction precision and accuracy are evaluated by relative root mean square error (RMSE %) and relative mean difference (D %).
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Quantification of soil spatial variability across multiple scales is important in ecological modeling, environmental prediction, precision agriculture, and natural resources management.
Therefore, it is important to have an insightful understanding of the contribution of the additional diameter measurement to taper model prediction bias and precision to better inform decisions on whether to develop advanced technology for, or invest in, tree upper stem diameter measurements(s) for taper equation use.
It has been shown that the dynamic models have higher prediction precision and give more information compared with the steady thermodynamic models.
The neural network model shows a higher prediction precision that the polynomial model.
Experimental results have shown that the proposed prediction model provides a satisfactory prediction precision for different types of clips.
The modified model presents a higher prediction precision with average relative difference of 1.24% at LFL.
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