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

Guo-xing Ren; Zhi-qiang Wei; Meirong Lv

doi:10.1117/12.2551264

31 January 2020 Prediction of organic carbon content in sediments of Jiaozhou Bay beach by visible-near infrared spectroscopy based on least squares support vector machine

Guo-xing Ren, Zhi-qiang Wei, Meirong Lv

Author Affiliations +

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 1142716 (2020) https://doi.org/10.1117/12.2551264
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

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.

Citation Download Citation

Guo-xing Ren, Zhi-qiang Wei, and Meirong Lv "Prediction of organic carbon content in sediments of Jiaozhou Bay beach by visible-near infrared spectroscopy based on least squares support vector machine", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 1142716 (31 January 2020); https://doi.org/10.1117/12.2551264

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KEYWORDS

Data modeling

Near infrared

Visible radiation

Carbon

Error analysis

Statistical modeling

Infrared spectroscopy

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