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Standing volume prediction of Pinus sylvestris var. mongolica based on machine learning algorithm
SUN Mingchen, JIANG Lichun
JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (1) : 31-37.
PDF(1588 KB)
PDF(1588 KB)
Standing volume prediction of Pinus sylvestris var. mongolica based on machine learning algorithm
【Objective】 Using various, nonlinear machine learning algorithms, different volume models were constructed and compared to provide a theoretical basis for the accurate prediction of the volume of Pinus sylvestris var. mongolica.【Method】 A total of 184 felled Pinus sylvestris var. mongolica trees in the Tuqiang Forestry Bureau of the Greater Khingan Mountains were used to establish a nonlinear binary volume model (NLR). Three optimal machine learning algorithms were obtained using the K-fold cross test and OOB error test, including back propagation neural network (BP), ε-support vector regression (ε-SVR), and random forest (RF). An optimal volume model was obtained by comparing and analyzing the differences between the different models. 【Result】 The results showed that the machine learning algorithm was superior to the traditional binary volume model in the fitting and prediction of standing volume, and the specific order was RF > BP > ε-SVR > NLR. Compared with the traditional model, the R2 of RF increased by 2.00%; the RMSE, RMSE% and MAE decreased by 22.95%, 22.93% and 36.34%, respectively; and the absolute value of MRB was lower than the real value, which proved the superiority of RF in volume prediction. 【Conclusion】 Machine learning algorithms can effectively improve the accuracy at which standing volume can be predicted, providing a new solution for the accurate investigation and management of forest resources.
Pinus sylvestris var. mongolica / binary volume model / BP neural network / ε-support vector regression(ε-SVR) / random forest(RF)
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