Exploring optical descriptors for rapid estimation of coastal sediment organic carbon and nearby land-use classifications via machine learning models

Xuan Cuong Nguyen; Suhyeon Jang; Junsung Noh; Jong Seong Khim; Junghyun Lee; Bong-Oh Kwon; Tieyu Wang; Wenyou Hu; Xiaowei Zhang; Hai Bang Truong; Jin Hur

doi:10.1016/j.marpolbul.2024.116307

Exploring optical descriptors for rapid estimation of coastal sediment organic carbon and nearby land-use classifications via machine learning models

Mar Pollut Bull. 2024 May:202:116307. doi: 10.1016/j.marpolbul.2024.116307. Epub 2024 Apr 1.

Authors

Xuan Cuong Nguyen¹, Suhyeon Jang², Junsung Noh², Jong Seong Khim³, Junghyun Lee⁴, Bong-Oh Kwon⁵, Tieyu Wang⁶, Wenyou Hu⁷, Xiaowei Zhang⁸, Hai Bang Truong⁹, Jin Hur¹⁰

Affiliations

¹ Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam; Faculty of Environmental Chemical Engineering, Duy Tan University, Da Nang 550000, Viet Nam; Department of Environment and Energy, Sejong University, Seoul 05006, South Korea.
² Department of Environment and Energy, Sejong University, Seoul 05006, South Korea.
³ School of Earth and Environmental Sciences & Research Institute of Oceanography, Seoul National University, Seoul 08826, South Korea.
⁴ Department of Environmental Education, Kongju National University, Gongju 32588, South Korea.
⁵ Department of Marine Biotechnology, Kunsan National University, Kunsan 54150, Republic of Korea.
⁶ Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China.
⁷ Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
⁸ State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
⁹ Optical Materials Research Group, Science and Technology Advanced Institute, Van Lang University, Ho Chi Minh City 700000, Viet Nam; Faculty of Applied Technology, School of Technology, Van Lang University, Ho Chi Minh City 70000, Viet Nam.
¹⁰ Department of Environment and Energy, Sejong University, Seoul 05006, South Korea. Electronic address: jinhur@sejong.ac.kr.

PMID: 38564820
DOI: 10.1016/j.marpolbul.2024.116307

Abstract

This study utilizes ultraviolet and fluorescence spectroscopic indices of dissolved organic matter (DOM) from sediments, combined with machine learning (ML) models, to develop an optimized predictive model for estimating sediment total organic carbon (TOC) and identifying adjacent land-use types in coastal sediments from the Yellow and Bohai Seas. Our results indicate that ML models surpass traditional regression techniques in estimating TOC and classifying land-use types. Penalized Least Squares Regression (PLR) and Cubist models show exceptional TOC estimation capabilities, with PLR exhibiting the lowest training error and Cubist achieving a correlation coefficient 0.79. In land-use classification, Support Vector Machines achieved 85.6 % accuracy in training and 92.2 % in testing. Maximum fluorescence intensity and ultraviolet absorbance at 254 nm were crucial factors influencing TOC variations in coastal sediments. This study underscores the efficacy of ML models utilizing DOM optical indices for near real-time estimation of marine sediment TOC and land-use classification.

Keywords: Dissolved organic matter (DOM); Fluorescence; Land-use; Machine learning; Total organic carbon; Ultraviolet.

MeSH terms

Carbon* / analysis
Environmental Monitoring* / methods
Geologic Sediments* / chemistry
Machine Learning*

Substances

Carbon