A physics-informed statistical learning framework for forecasting local suspended sediment concentrations in marine environment

Journal article

Zhang, Shaotong, Wu, Ryan, Wang, You-Gan, Jeng, Dong-Sheng and Li, Guangxue. (2022). A physics-informed statistical learning framework for forecasting local suspended sediment concentrations in marine environment. Water Research. 218, pp. 1-16. https://doi.org/10.1016/j.watres.2022.118518
AuthorsZhang, Shaotong, Wu, Ryan, Wang, You-Gan, Jeng, Dong-Sheng and Li, Guangxue
Abstract

An in-situ monitoring of water quality (suspended sediment concentration, SSC) and concurrent hydrodynamics was conducted in the subaqueous Yellow River Delta in China. Empirical mode decomposition and spectral analysis on the SSC time series reveal the different periodicities of each physical mechanism that contribute to the SSC variations. Based on this physical understanding, the decomposed SSC time series were trained separately with a newly-proposed augmented lncosh ridge regression, in which (1) a lncosh function was incorporated in traditional ridge regression for handling outliers in original data, and (2) the temporal auto-correlation in the decomposed SSC series was used for augmented regression. Finally, the trained sub-series were added up as the final prediction. The advantages of this decomposition-ensemble framework is that it depends on SSC only, superior to the normal process-based models which need the concurrent hydrodynamics for estimating bed shear stress. This will not only reduce the measurement uncertainties of the input when training the data-driven model, but also save the prediction cost as no other parameters than SSC need to be measured and input for running the model. The framework realized 6-hour-ahead high-accuracy forecasting with mean relative errors of 5.80-9.44% in the present case study. The proposed framework can be extended to forecast any signal that is superposed by components with various timescales (periodicities) which is common in nature.

KeywordsAugmented lncosh ridge regression; Marine ranching; Outlier handling; Temporal auto-correlation; The Yellow River Delta; Water quality
Year01 Jan 2022
JournalWater Research
Journal citation218, pp. 1-16
PublisherElsevier Ltd.
ISSN0043-1354
Digital Object Identifier (DOI)https://doi.org/10.1016/j.watres.2022.118518
PubMed ID35526355
Scopus EID2-s2.0-85129552167
Web address (URL)https://www.sciencedirect.com/science/article/pii/S0043135422004638
Open accessPublished as non-open access
Research or scholarlyResearch
Page range1-16
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online27 Apr 2022
Publication process dates
Accepted21 Apr 2022
Deposited11 Jan 2023
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDDP160104292
ZR2019BD009
41807229
2018M640656
201801026
IC190100020
Additional information

© 2022 Elsevier Ltd. All rights reserved.

This work was supported in part by Australian Research Council (ARC) Discovery Project ( DP160104292 ).

This study was supported by the Natural Science Foundation of Shandong Province (grant number ZR2019BD009 ), the Natural Science Foundation of China (grant number 41807229 ), the China Postdoctoral Science Foundation (grant number 2018M640656 ), Shandong Provincial Postdoctoral Program for Innovative Talents (grantee Shaotong Zhang), and the Funding for Study Abroad Program by the Government of Shandong Province (grant number 201801026 ). This work was supported in part by Industrial Transformation Training Centres (IC190100020).

Place of publicationUnited Kingdom
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