基于Stacking集成学习的土壤侵蚀速率计算与主导因子分析——以三峡库区奉节县为例

doi:10.16843/j.sswc.2023.04.012

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Abstract [Background] The calculation and assessment of soil erosion is the key to soil and water conservation. In order to improve the calculation accuracy, stacking ensemble method is introduced, which can fully integrate different machine learning models to obtain high-precision spatial distribution data of soil erosion rate. At the same time, the leading factors affecting the soil erosion rate in the study area were analyzed.[Methods] Firstly, the feature dataset was constructed based on the data of 2018 rainfall, remote sensing images and others in Fengjie county, Chongqing, and the actual data of soil erosion rate in Fengjie county was used as the benchmark to train different machine learning models. Then, the accuracy evaluation index and diversity measure were used to establish the optimal combination of base-learners and meta-learner, construct the stacking integrated model, and to calculate the soil erosion rate in the whole county. Finally, the marginal dependence of the leading factors was analyzed according to the distribution law of soil erosion rate.[Results] 1) The stacking ensemble model with light gradient boosting machineand random forest as the base-learners and linearregressionas the meta-learner has the best effect. The M_AE(mean absolute error), R_MSE (root mean square error) and accuracy of R²(R-squared) are as follows:252.48 t/(km²·a), 537.78 t/(km²·a) and 0.868 7. 2) Elevation, rainfall, vegetation cover, slope, distance from the road and distance from water source were the top 6 factors influencing soil erosion rate in Fengjie county,with importance accounting for more than 9%. 3) Soil erosion rate was higher in the region with an elevation of 200-520 m, annual rainfall higher than 1 250 mm, NDVI (normalized difference vegetation index) of 0.24-0.27, slope of 26°-35°, distance from the road to 0-220 m, and distance from the water source to 63-387 m.[Conclusions] The results show that the stacking model constructed in this paper can effectively integrate different models and improve the accuracy of predicting soil erosion rate. Soil erosion rate in Fengjie county is affected by many factors.In general, soil erosion rate was positively correlated with elevation and vegetation cover degree, and negatively correlated with rainfall and slope.The higher rate of soil erosion tended to occur in steep low-elevation areas with abundant rainfall, low vegetation cover, and close proximity to roads and water sources.

Key words： machine learning soil erosion Stacking optimization and integration leading factor analysis

Received: 22 July 2022

PACS:	X87
	P237

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	Articles by authors
	LIN Na
	PAN Peng
	WANG Bin
	ZHANG Di
	FENG Shanshan
	PAN Jianping

Cite this article:

LIN Na,PAN Peng,WANG Bin, et al. Soil erosion rate calculation based on Stacking ensemble learning and leading factor analysis: A case study of Fengjie county in the Three Gorges Reservoir Area[J]. SSWC, 2023, 21(4): 100-112.

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http://journal12.magtechjournal.com/Jweb_stbc/EN/10.16843/j.sswc.2023.04.012 OR http://journal12.magtechjournal.com/Jweb_stbc/EN/Y2023/V21/I4/100

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