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Proceedings Paper

Prediction of organic carbon content in sediments of Jiaozhou Bay beach by visible-near infrared spectroscopy based on least squares support vector machine
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Paper Abstract

Visible-near infrared reflection spectroscopy has the advantages of fast and green, and has great technical advantages in the field detection of soil components, but no study on organic carbon (OC) in marine beach sediments has been found. According to the OC content and visible - near infrared reflection spectrum of sediment samples from Jiaozhou Bay beach in Qingdao, the spectral preprocessing was carried out by S-G derivative filter, and the sediment samples were divided by Kennard-Stone algorithm. On the basis of the whole band, the prediction model of sediment OC is established by using the least square support vector machine (LSSVM) algorithm. According to the evaluation results of the model, the modeling set R2=0.97, the prediction set R2=0.83, and the relative analysis error RPD=2.46, shows that the model has a good prediction effect.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 1142716 (31 January 2020); doi: 10.1117/12.2551264
Show Author Affiliations
Guo-xing Ren, Qilu Univ. of Technology (China)
Ocean Univ. of China (China)
Zhi-qiang Wei, Ocean Univ. of China (China)
Meirong Lv, Qilu Univ. of Technology (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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