Application of the machine learning LightGBM model to the prediction of the water levels of the lower Columbia River

Gan, Min, Pan, Shunqi ORCID: https://orcid.org/0000-0001-8252-5991, Chen, Yongping, Cheng, Chen, Pan, Haidong and Zhu, Xian 2021. Application of the machine learning LightGBM model to the prediction of the water levels of the lower Columbia River. Journal of Marine Science and Engineering 9 (5) , 496. 10.3390/jmse9050496

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Abstract

Due to the strong nonlinear interaction with river discharge, tides in estuaries are characterised as nonstationary and their mechanisms are yet to be fully understood. It remains highly challenging to accurately predict estuarine water levels. Machine learning methods, which offer a unique ability to simulate the unknown relationships between variables, have been increasingly used in a large number of research areas. This study applies the LightGBM model to predicting the water levels along the lower reach of the Columbia River. The model inputs consist of the discharges from two upstream rivers (Columbia and Willamette Rivers) and the tide characteristics, including the tide range at the estuary mouth (Astoria) and tide constituents. The model is optimized with the selected parameters. The results show that the LightGBM model can achieve high prediction accuracy, with the root-mean-square-error values of water level being reduced to 0.14 m and the correlation coefficient and skill score being in the ranges of 0.975–0.987 and 0.941–0.972, respectively, which are statistically better than those obtained from physics-based models such as the nonstationary tidal harmonic analysis model (NS_TIDE). The importance of subtide constituents in interacting with the river discharge in the estuary is clearly revealed from the model results.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering Advanced Research Computing @ Cardiff (ARCCA)
Publisher:	MDPI
ISSN:	2077-1312
Date of First Compliant Deposit:	11 June 2021
Date of Acceptance:	26 April 2021
Last Modified:	06 Feb 2024 09:34
URI:	https://orca.cardiff.ac.uk/id/eprint/141852

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