Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks.

Autor: Nguyen TTH; Laboratory of Environmental Sciences and Climate Change, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam; Faculty of Environment, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam. Electronic address: ha.nguyenthithu@vlu.edu.vn., Vu DQ; Department of Computer Science and Information System, Thai Nguyen University of Education, Vietnam. Electronic address: quangvd@tnue.edu.vn., Doan NP; School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, United Kingdom. Electronic address: ndoan01@qub.ac.uk., Chi HTK; School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, United Kingdom. Electronic address: thuynh01@qub.ac.uk., Li P; School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, United Kingdom; College of Economics and Management, Nanjing University of Aeronautics and Astronautics, China. Electronic address: p.li@qub.ac.uk., Binh DV; Master program in Water Technology, Reuse and Management, Faculty of Engineering, Vietnamese-German University, Ben Cat City, Binh Duong Province, Vietnam. Electronic address: binh.dv@vgu.edu.vn., An Y; School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, United Kingdom; College of Economics and Management, Nanjing University of Aeronautics and Astronautics, China. Electronic address: y.an@qub.ac.uk., Dung PT; School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, United Kingdom. Electronic address: tpham01@qub.ac.uk., Hoang TA; School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, United Kingdom. Electronic address: t.hoang@qub.ac.uk., Son MT; School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, United Kingdom. Electronic address: thaison.mai@qub.ac.uk.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2024 Dec 10; Vol. 955, pp. 176758. Date of Electronic Publication: 2024 Oct 12.
DOI: 10.1016/j.scitotenv.2024.176758
Abstrakt: The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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