Acta Prataculturae Sinica ›› 2022, Vol. 31 ›› Issue (4): 177-188.DOI: 10.11686/cyxb2021072
Ge-xia QIN1(), Jing WU1(), Chun-bin LI1, Zhen-xia JI1, Zheng-chao QIU2, Ying LI1
Received:
2021-02-25
Revised:
2021-03-29
Online:
2022-04-20
Published:
2022-01-25
Contact:
Jing WU
Ge-xia QIN, Jing WU, Chun-bin LI, Zhen-xia JI, Zheng-chao QIU, Ying LI. Inversion of grassland aboveground biomass in Tianzhu Zangzu Autonomous County based on a machine learning algorithm[J]. Acta Prataculturae Sinica, 2022, 31(4): 177-188.
机器学习模型 Machine learning models | 决定系数R2 | 均方根误差 RMSE | 平均绝对误差 MAE | |||
---|---|---|---|---|---|---|
范围Range | 平均Mean | 范围Range | 平均Mean | 范围Range | 平均Mean | |
DNN | 0.80~0.88 | 0.85 | 25.43~32.30 | 32.30 | 4.23~6.14 | 5.63 |
GBRT | 0.74~0.88 | 0.84 | 21.51~49.63 | 32.31 | 4.55~7.29 | 5.95 |
RF | 0.75~0.88 | 0.84 | 30.41~43.48 | 37.21 | 4.55~6.63 | 5.58 |
SVR | 0.77~0.84 | 0.80 | 39.55~49.24 | 46.01 | 6.33~7.92 | 7.46 |
ANN | 0.74~0.87 | 0.81 | 38.04~54.19 | 45.99 | 4.95~9.36 | 6.54 |
GPR | 0.76~0.88 | 0.83 | 32.60~51.03 | 39.72 | 4.94~7.14 | 6.02 |
Table 1 Statistical table of R2, RMSE and MAErepeated 30 times
机器学习模型 Machine learning models | 决定系数R2 | 均方根误差 RMSE | 平均绝对误差 MAE | |||
---|---|---|---|---|---|---|
范围Range | 平均Mean | 范围Range | 平均Mean | 范围Range | 平均Mean | |
DNN | 0.80~0.88 | 0.85 | 25.43~32.30 | 32.30 | 4.23~6.14 | 5.63 |
GBRT | 0.74~0.88 | 0.84 | 21.51~49.63 | 32.31 | 4.55~7.29 | 5.95 |
RF | 0.75~0.88 | 0.84 | 30.41~43.48 | 37.21 | 4.55~6.63 | 5.58 |
SVR | 0.77~0.84 | 0.80 | 39.55~49.24 | 46.01 | 6.33~7.92 | 7.46 |
ANN | 0.74~0.87 | 0.81 | 38.04~54.19 | 45.99 | 4.95~9.36 | 6.54 |
GPR | 0.76~0.88 | 0.83 | 32.60~51.03 | 39.72 | 4.94~7.14 | 6.02 |
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