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Proceedings Paper

Modeling based on carbon content classification does not improve the prediction accuracy of carbon in intertidal zone sediments
Author(s): Meirong Lv; Xie-ying Li; Ping-ping Fan; Jie Liu; Zhong-liang Sun; Guang-li Hou; Yan Liu
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Paper Abstract

The rapid prediction of carbon content by spectral method is helpful to grasp the carbon dynamic of sediments in intertidal zone in a timely manner. The spectral reflectivity of sediments with high carbon content and low carbon content often varies widely, limiting the accuracy of the model. When sediment samples are classified according to the carbon content and modeled separately, accurate prediction of sediment carbon is expected to be achieved. However, previous studies have not reported it. In our study, sediment samples were divided into low-carbon and high-carbon sample sets according to 3 g/kg carbon content, and divided into low-carbon, medium-carbon and high-carbon sample sets according to 2 g/kg and 4 g/kg carbon content, and then were pre-processed and PLS modeled separately. It is found that these classification can improve the modeling accuracy, but not improve the prediction accuracy. These results can provide a technical reference for the prediction of sediment carbon in intertidal zone.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270V (31 January 2020);
Show Author Affiliations
Meirong Lv, Qilu Univ. of Technology (China)
National Engineering and Technological Research Ctr. of Marine Monitoring Equipment (China)
Xie-ying Li, Qilu Univ. of Technology (China)
National Engineering and Technological Research Ctr. of Marine Monitoring Equipment (China)
Ping-ping Fan, Qilu Univ. of Technology (China)
National Engineering and Technological Research Ctr. of Marine Monitoring Equipment (China)
Jie Liu, Qilu Univ. of Technology (China)
National Engineering and Technological Research Ctr. of Marine Monitoring Equipment (China)
Zhong-liang Sun, Qilu Univ. of Technology (China)
National Engineering and Technological Research Ctr. of Marine Monitoring Equipment (China)
Guang-li Hou, Qilu Univ. of Technology (China)
National Engineering and Technological Research Ctr. of Marine Monitoring Equipment (China)
Yan Liu, Qilu Univ. of Technology (China)
National Engineering and Technological Research Ctr. of Marine Monitoring Equipment (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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