A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability

Journal article


Zhang, Shaotong, Wu, Ryan, Jia, Yonggang, Wang, You-Gan, Zhang, Yaqi and Duan, Qibin. (2021). A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability. Engineering Applications of Artificial Intelligence. 100, pp. 1-13. https://doi.org/10.1016/j.engappai.2021.104206
AuthorsZhang, Shaotong, Wu, Ryan, Jia, Yonggang, Wang, You-Gan, Zhang, Yaqi and Duan, Qibin
Abstract

In situ observations of suspended sediment concentration (SSC) and hydrodynamics were conducted in the subaqueous Yellow River Delta, China. With the dataset, a new least absolute shrinkage and selection operator (LASSO) regression model with temporal autocorrelation incorporated (temporal LASSO) is proposed for SSC prediction and mechanism investigation in coastal oceans. The model is concise and practical, effectively shrinking the interrelated variables into representative ones, while also achieving one-hour ahead forecasting with both higher accuracy and better interpretability than other data-driven methods. The model interpretability is further validated with direct data analysis from a physical perspective. Specifically, Empirical Mode Decomposition is employed to decouple the measured SSC into intrinsic mode functions (IMFs) and a residual. The periods of each subseries estimated from both zero-crossing and spectrum analysis show that IMF1 physically corresponds to the sediment resuspension by M4 tidal currents, IMF2 is the M2 tidal advection, IMF3-IMF5 are the resuspension by wind waves, IMF6 is the spring–neap tidal pumping of sediments. The contributions estimated with the ratio of variance are 12 %, 14 %, 63 %, and 10 %, respectively, over the observation period. The residual is the seasonal variations which can be taken as the background SSC thus not included for variance contribution. Waves make the dominant contribution which verifies the rationality of the LASSO shrinkage and confirms the model interpretability. The temporal LASSO model is shown to be a potential tool for emergency forecasting and mechanism explanation of SSC to benefit ocean environmental engineering management.

KeywordsMachine learning; Empirical mode decomposition; Spectrum analysis; Sediment resuspension; Tidal advection; Static settling
Year01 Jan 2021
JournalEngineering Applications of Artificial Intelligence
Journal citation100, pp. 1-13
PublisherElsevier Ltd.
ISSN0952-1976
Digital Object Identifier (DOI)https://doi.org/10.1016/j.engappai.2021.104206
Web address (URL)https://www.sciencedirect.com/science/article/pii/S0952197621000531
Open accessPublished as non-open access
Research or scholarlyResearch
Page range1-13
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Output statusPublished
Publication dates
Print24 Feb 2021
Publication process dates
Accepted14 Feb 2021
Deposited11 Jan 2023
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© 2021 Elsevier Ltd. All rights reserved.

Place of publicationUnited Kingdom
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