Modeling based on carbon content classification does not improve the prediction accuracy of carbon in intertidal zone sediments

Meirong Lv; Xie-ying Li; Ping-ping Fan; Jie Liu; Zhong-liang Sun; Guang-li Hou; Yan Liu

doi:10.1117/12.2550607

31 January 2020 Modeling based on carbon content classification does not improve the prediction accuracy of carbon in intertidal zone sediments

Meirong Lv, Xie-ying Li, Ping-ping Fan, Jie Liu, Zhong-liang Sun, Guang-li Hou, Yan Liu

Author Affiliations +

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114270V (2020) https://doi.org/10.1117/12.2550607
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

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.

Citation Download Citation

Meirong Lv, Xie-ying Li, Ping-ping Fan, Jie Liu, Zhong-liang Sun, Guang-li Hou, and Yan Liu "Modeling based on carbon content classification does not improve the prediction accuracy of carbon in intertidal zone sediments", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270V (31 January 2020); https://doi.org/10.1117/12.2550607

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