Machine Learning Models for Evaluating Biological Reactivity Within Molecular Fingerprints of Dissolved Organic Matter Over Time

Zhao，Chen1; Wang，Kai2; Jiao，Qianji3; Xu，Xinyue4; Yi，Yuanbi1; Li，Penghui5,6,7; Merder，Julian8; He，Ding1,9

2024-06-16

10.1029/2024GL108794

Geophysical Research Letters   影响因子和分区

0094-8276

1944-8007

Reservoirs exert a profound influence on the cycling of dissolved organic matter (DOM) in inland waters by altering flow regimes. Biological incubations can help to disentangle the role that microbial processing plays in the DOM cycling within reservoirs. However, the complex DOM composition poses a great challenge to the analysis of such data. Here we tested if the interpretable machine learning (ML) methodologies can contribute to capturing the relationships between molecular reactivity and composition. We developed time-specific ML models based on 7-day and 30-day incubations to simulate the biogeochemical processes in the Three Gorges Reservoir over shorter and longer water retention periods, respectively. Results showed that the extended water retention time likely allows the successive microbial degradation of molecules, with stochasticity exerting a non-negligible effect on the molecular composition at the initial stage of the incubation. This study highlights the potential of ML in enhancing our interpretation of DOM dynamics over time.

biological degradation dissolved organic matter machine learning molecular composition Three Gorges Reservoir

SCI ; EI

英语

其他

GEOSCIENCES

2-s2.0-85195263710

Scopus

1.Department of Ocean Science,Center for Ocean Research in Hong Kong and Macau,The Hong Kong University of Science and Technology,Hong Kong
2.Department of Ocean Science and Engineering,Southern University of Science and Technology,Shenzhen,China
3.School of Mathematics and Statistics,Xidian University,Xi'an,China
4.Department of Electronic and Computer Engineering,The Hong Kong University of Science and Technology,Hong Kong
5.School of Marine Sciences,Sun Yat-sen University,Zhuhai,China
6.Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),Zhuhai,China
7.Guangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering,Zhuhai,China
8.Department of Global Ecology,Carnegie Institution for Science,Stanford,United States
9.State Key Laboratory of Marine Pollution,City University of Hong Kong,Hong Kong

Zhao，Chen,Wang，Kai,Jiao，Qianji,et al. Machine Learning Models for Evaluating Biological Reactivity Within Molecular Fingerprints of Dissolved Organic Matter Over Time[J]. Geophysical Research Letters,2024,51(11).

Zhao，Chen.,Wang，Kai.,Jiao，Qianji.,Xu，Xinyue.,Yi，Yuanbi.,...&He，Ding.(2024).Machine Learning Models for Evaluating Biological Reactivity Within Molecular Fingerprints of Dissolved Organic Matter Over Time.Geophysical Research Letters,51(11).

Zhao，Chen,et al."Machine Learning Models for Evaluating Biological Reactivity Within Molecular Fingerprints of Dissolved Organic Matter Over Time".Geophysical Research Letters 51.11(2024).