基于主成分和机器学习的土壤有机质含量空间预测建模

doi:10.12118/j.issn.1000–6060.2021.04.23

Abstract

Abstract:

Spatial prediction models of soil nutrients are constructed from collaborative environment variables and machine learning regression models; they are of great significance for accurate nutrient management, but the information redundancy and correlation among multidimensional variables can lead to problems such as a long training time for the model and low prediction accuracy. In this study, the farming area of Xianyang City, Shaanxi Province, China, was taken as an example, and 10 environmental variables were selected: the elevation, aspect, slope, plane curvature, section curvature, relief, topographic wetness index, annual average temperature, annual average precipitation, and normalized difference vegetation index. Features were extracted by principal component analysis (PCA) and kernel PCA (KPCA), which were combined with the random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN) models to develop spatial prediction models for the soil organic matter (SOM). Single models were used as the control. Then, the prediction accuracy of different models was evaluated according to the model determination coefficient (R²), root-mean-squared error (RMSE), and relative absolute error (RAE). The following results were obtained: (1) PCA and KPCA reduced the data dimensionality, which eliminated the correlation and redundancy between variables and helped improve the accuracy and stability of the SOM spatial prediction model. (2) The PCA-RF model had a higher prediction accuracy than the RF model (R² increased by 0.023, RMSE and RAE decreased by 0.070 g·kg^-1 and 2.440%, respectively), whereas PCA-SVR and PCA-KNN performed worse than SVR and KNN alone. (3) The KPCA-RF model had higher accuracy than the RF model (R², RMSE, and RAE were 0.791, 1.970 g·kg^-1, and 50.100%, respectively). The KPCA-SVR and KPCA-KNN models had better prediction accuracies than the SVR and KNN models. (4) The combined prediction model based on KPCA feature extraction and machine learning had higher prediction accuracy than the PCA-based combined prediction models and single prediction models and fitted well to the nonlinear relationship between the SOM content and environmental variables. The KPCA-RF model performed better than the other prediction models. This model accurately predicted the SOM content in the agricultural area of Xianyang City, and it can be further applied to accurately predicting other soil nutrients and evaluating soil fertility.

Key words: soil organic matter, machine learning, kernel principal component analysis, farming area, Xianyang City

HU Guigui,YANG Fenli,YANG Lian’an,ZHENG Yurong,WANG Hui,CHEN Weijun,LI Yali. Spatial prediction modeling of soil organic matter content based on principal components and machine learning[J].Arid Land Geography, 2021, 44(4): 1114-1124.

Figures/Tables 8

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References 46

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指标/个	样点数/个	最小值/g·kg^-1	最大值/g·kg^-1	平均值/g·kg^-1	标准差/g·kg^-1	变异系数/%
整体	407	6.59	27.80	15.54	3.80	24.45
训练集	285	6.59	24.78	15.64	3.79	24.23
验证集	122	7.21	27.80	15.34	3.84	25.36

	SOM	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10
SOM	1.000
X1	-0.315^**	1.000
X2	-0.099^*	0.317^**	1.000
X3	-0.006	0.123^*	0.082	1.000
X4	-0.006	-0.073	0.166^**	0.002	1.000
X5	0.003	-0.016	-0.153^**	-0.205^**	-0.262^**	1.000
X6	-0.130^**	0.336^**	0.936^**	0.058	0.174^**	-0.074	1.000
X7	0.101^*	-0.340^**	-0.475^**	-0.053	0.222^**	-0.181^**	-0.458^**	1.000
X8	0.206^**	-0.417^**	-0.226^**	-0.053	-0.057	0.099^*	-0.229^**	0.084	1.000
X9	0.285^**	-0.981^**	-0.342^**	-0.137^**	0.021	0.038	-0.362^**	0.306^**	0.494^**	1.000
X10	0.204^**	-0.235^**	-0.089	0.004	-0.026	-0.016	-0.063	0.085	0.117^*	0.185^**	1.000

主成分	主成分分析（PCA）			核主成分分析（KPCA）
主成分	特征值	贡献率/%	累积贡献率/%	贡献率/%	累积贡献率/%
1	3.22	32.21	32.21	82.41	82.41
2	1.63	16.28	48.48	15.58	97.99
3	1.45	14.48	62.97	-	-
4	1.11	11.06	74.03	-	-
5	0.93	9.28	83.31	-	-
6	0.67	6.69	90.00	-	-

变量	主成分
变量	1	2	3	4	5	6
X1	0.198	-0.952	0.001	-0.063	0.011	0.057
X2	0.935	-0.148	-0.113	0.113	-0.127	0.016
X3	0.031	-0.077	-0.019	-0.003	-0.106	0.987
X4	0.093	0.029	-0.067	0.953	-0.104	0.006
X5	-0.037	0.015	0.061	-0.122	0.972	-0.111
X6	0.925	-0.180	-0.074	0.149	-0.048	-0.006
X7	-0.672	0.187	-0.071	0.404	-0.247	-0.074
X8	-0.063	0.539	0.653	-0.007	0.072	0.043
X9	-0.203	0.962	0.055	0.009	0.005	-0.063
X10	-0.069	-0.075	0.919	-0.077	0.032	-0.041

模型	R²	RMSE/g·kg^-1	RAE/%
PCA-RF	0.739	2.090	49.473
KPCA-RF	0.791	1.970	50.100
RF	0.716	2.160	51.913
PCA-SVR	0.479	2.936	66.718
KPCA-SVR	0.561	2.361	51.302
SVR	0.557	2.783	53.072
PCA-KNN	0.463	2.821	70.273
KPCA-KNN	0.507	2.676	66.079
KNN	0.490	2.744	68.553

Spatial prediction modeling of soil organic matter content based on principal components and machine learning

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模型	参数设定
PCA-RF	ntree=1300、mtry=3
KPCA-RF	ntree=1300、mtry=2
PCA-SVR	radial核函数、C=1000、gramma=0.1
KPCA-SVR	radial核函数、C=1000、gramma=0.01
PCA-KNN	K=11
KPCA-KNN	K=3

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