基于无人机遥感和集成学习的苏州市河流悬浮物浓度反演

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Abstract The inversion of suspended sediment concentrations of urban rivers by remote sensing has important practical significance for water environmental management. To address the problem of overfitting in individual models, this study attempts to improve the accuracy and generalizability of the inversion model by realizing the complementary advantages among four different ensemble learning strategies. Ensemble learning inversion models were established based on multispectral remote sensing images collected by unmanned aerial vehicles and field-measured suspended sediment concentrations of Suzhou in this study. Four commonly used regression methods and three classic machine learning methods were used to validate the effectiveness of the ensemble learning strategies. The results demonstrate that the ensemble learning strategies effectively mitigate the limitations of individual models, substantially improving the accuracy and generalizability of the remote sensing inversions. The stacking strategy achieves the best performance with a validation set's coefficient of determination of 0.821, show casing a 38.21% enhancement compared with the regression methods and a 16.79% improvement compared to the individual machine learning methods. The absolute error of the ensemble learning method is concentrated on small values, with its mean and median absolute errors surpassing the traditional methods. This study can improve the accuracy of urban suspended sediment concentration inversion and provide guidance for unmanned aerial vehicle remote sensing of river suspended sediment concentration inversion.

Key words： river suspended sediment concentration unmanned aerial vehicle remote sensing multispectral ensemble learning machine learning

Received: 01 March 2023

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	Articles by authors
	YU Cheng
	TANG Yi
	PAN Yang
	YI Hong-chen
	GU Yi-ping
	ZHU Feng
	SHI Jiao-yang

Cite this article:

YU Cheng,TANG Yi,PAN Yang等. Inversion of suspended sediment concentration in rivers of Suzhou based on UAV remote sensing and ensemble learning[J]. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(10): 5235-5246.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2023/V43/I10/5235

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