干旱区生态环境知识资源中心(Arid): Estimation of Arsenic Content in Soil Based on Continuous Wavelet Transform

Arid

DOI	10.3964/j.issn.1000-0593(2023)01-0206-07
	Estimation of Arsenic Content in Soil Based on Continuous Wavelet Transform
	Wang Xue-mei; Yumiti Maiming; Huang Xiao-yu; Li Rui; Liu Dong
通讯作者	Wang, XM
来源期刊	SPECTROSCOPY AND SPECTRAL ANALYSIS
ISSN	1000-0593
出版年	2023
卷号	43 期号:1 页码:206-212
英文摘要	Compared with the traditional detection methods, hyperspectral technology has the characteristics of rapid, accurate and low cost in estimating soil heavy metal arsenic content and can dynamically monitor heavy metal arsenic pollution of oasis soils in arid regions. Based on the collection of soil samples from the cultivated layer of the delta oasis of Weigan-Kuqa river in Xinjiang, soil spectral data and heavy metal arsenic content were obtained. Through the four wavelet basis functions biorl. 3, db4, gaus4 and mexh, the original spectral reflectance of the soil was subjected to continuous wavelet transformation. The transformed spectral data was correlated with the heavy metal arsenic so that the selected sensitive wavelet coefficients were taken as independent variables, using partial least square regression, support vector machine regression, BP neural network and random forest regression methods to perform hyperspectral inversion of heavy metal arsenic content. The results showed that: (1) The spectral decomposition effect of the four wavelet basis functions at scales 3 to 8 was obviously better than that of other scales, especially the continuous wavelet transform at scales 4 to 6, effectively improved the correlation between the spectral reflectance with soil heavy metal arsenic, and the number of wavelet coefficients passing the significance test increased significantly (p<0. 01), and there had a strong correlation in the vicinity of 400 similar to 700 nm in visible light and 1 100 similar to 1 700 and 2 200 similar to 2 400 nm in near-infrared. (2) By comparing the ability of the four wavelet basis functions to identify effective information in the spectral data, it was believed that the wavelet basis functions biorl. 3 and mesh were better than db4 and gaus4. Among them, biorl. 3 had the best spectral decomposition effect, and gaus4 was relatively weak. Through the spectral transformation of the 5th scale of biorl. 3, the number of bands significantly related to soil heavy metal arsenic was the largest, which was 507 (p<0. 01). (3) Comparing the inversion results of the four modeling methods, it was found that the SVMR, BPNN and RFR models had stronger estimation capabilities than the PLSR model, and the estimation accuracy of the model was high. After comprehensively analyzing each model's stability and estimation accuracy, it was concluded that the biorl. 3-2(5)-RFR model could be used as the best estimation model for the heavy metal arsenic in the study area. The R-2 of the training set and the validation set of the model were 0. 893 and 0. 639 respectively, the RMSE were 1. 075 and 1. 651 mg center dot kg (1), and the RPD were 2. 89 and 1. 64 respectively, indicating that the model had a better estimation effect and powerful stability. Using appropriate wavelet basis functions to carry out continuous wavelet transform can reduce the white noise in hyperspectral soil data, excavate the effective information in soil spectral data, and provide a strong technical guarantee for accurate estimation of soil heavy metal arsenic content.
英文关键词	Wavelet basis function Decomposition scale Wavelet coefficient Machine learning algorithm model Arsenic
类型	Article
语种	中文
收录类别	SCI-E
WOS记录号	WOS:000934923800032
WOS类目	Spectroscopy
WOS研究方向	Spectroscopy
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398713
推荐引用方式 GB/T 7714	Wang Xue-mei,Yumiti Maiming,Huang Xiao-yu,et al. Estimation of Arsenic Content in Soil Based on Continuous Wavelet Transform[J],2023,43(1):206-212.
APA	Wang Xue-mei,Yumiti Maiming,Huang Xiao-yu,Li Rui,&Liu Dong.(2023).Estimation of Arsenic Content in Soil Based on Continuous Wavelet Transform.SPECTROSCOPY AND SPECTRAL ANALYSIS,43(1),206-212.
MLA	Wang Xue-mei,et al."Estimation of Arsenic Content in Soil Based on Continuous Wavelet Transform".SPECTROSCOPY AND SPECTRAL ANALYSIS 43.1(2023):206-212.