Predicting Soil Available Phosphorus by Hyperspectral Regression Method Based on Gradient Boosting Decision Tree

Fig. 5. Parameter optimization of GBDT model. (a) Rloss=Fls, Rn_estimators=100; (b) Rloss=Fhuber, Rn_estimators=200; (c) Rloss=Fquantile, Rn_estimators=200; (d) Rloss=Flad, Rn_estimators=310

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Fig. 6. Results of different model integration algorithms. (a) Results of random forest based on modeling set; (b) results of random forest based on testing set; (c) results of boosting tree based on modeling set; (d) results of boosting tree based on testing set; (e) results of GBDT based on modeling set; (f) results of GBDT based on testing set

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Table 1. Statistical parameters of soil available phosphorus content
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Table 1. Statistical parameters of soil available phosphorus content
Type Sample Max /(mg·kg^-1) Min /(mg·kg^-1) Average /(mg·kg^-1) Standard deviation /(mg·kg^-1)
Total 193 34.96 0.03 10.56 9.36
Training 144 34.96 0.03 10.94 9.49
Testing 49 32.24 0.60 9.01 8.99

Table 2. Testing results of optimal single model

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Table 2. Testing results of optimal single model

Modeling method	Training set		Testing set		Prediction level(testing set)	Parameter
Modeling method	f_RPD			R²	f_RPD	R²
PLS	1.66	0.73	1.65	0.68	B	R_LVs=11
RBF-PLS	1.58	0.71	1.79	0.73	B	R_LVs=11, R_gamma =0.016
Sigmoid-PLS	1.55	0.70	1.75	0.73	B	R_LVs =10, $\begin{matrix} {R_{gamma}}_{} \end{matrix}$ =0.00085,R_cofe0cofe0=4.5
SVR	1.60	0.74	1.53	0.69	B	C=10000
RBF-SVR	1.70	0.76	1.66	0.72	B	C=2000000, R_gamma =0.0028
Sigmoid-SVR	1.59	0.73	1.55	0.70	B	C=10¹¹, R_gamma =0.000001,R_cofe0=0
Ridge	1.60	0.74	1.50	0.69	B	R_Alpha=0.001
RBF-Ridge	1.55	0.74	1.50	0.70	B	R_Alpha=0.00006, R_gamma =0.01
Sigmoid-Ridge	1.52	0.72	1.50	0.69	B	R_Alpha=4×10^-7,R_gamma =0.0005, R_cofe0=0.9

Table 3. Results of multi-model combination

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Table 3. Results of multi-model combination

Ensemble method	Training set		Testing set		Prediction level(testing set)	Parameter
Ensemble method	f_RPD			R²	f_RPD	R²
Random forest	2.10	0.84	2.08	0.84	A	R_{n_estimators}, R_{max_depth}=5
Boosting tree	2.86	0.90	2.12	0.82	A	R_{n_estimators} =300, R_{learning_rate}=0.01,R_{max_depth}=5, R_loss=F_linear
GBDT	2.56	0.88	2.55	0.86	A	R_{n_estimators} =310, R_{learning_rate}=0.29,R_{max_depth}=4, R_loss=F_lad

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Xiu Jin, Xianzhi Zhu, Shaowen Li, Wencai Wang, Haijun Qi. Predicting Soil Available Phosphorus by Hyperspectral Regression Method Based on Gradient Boosting Decision Tree[J]. Laser & Optoelectronics Progress, 2019, 56(13): 131102

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