An improved machine learning-based model to predict estuarine water levels

Gan, Min, Chen, Yongping, Pan, Shunqi ORCID: https://orcid.org/0000-0001-8252-5991, Lai, Xijun, Pan, Haidong, Wen, Yuncheng and Xia, Mingyan 2024. An improved machine learning-based model to predict estuarine water levels. Ocean Modelling 190 , 102376. 10.1016/j.ocemod.2024.102376 Item availability restricted.

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Abstract

The areas around estuaries are typically densely populated and economically developed. Therefore, robust flood risk assessment in these areas is critical. One of the key elements of flood risk assessment is the accurate prediction of estuarine water levels. However, the nonlinear interactions between riverine (i.e., upstream river discharge) and marine (i.e., tides) forces complicate the prediction of estuarine water levels. Traditional physics-based and data-driven models have made significant progress in predicting estuarine water levels, but they require upstream river discharge data as inputs. Considering the lack of such data, the development of new approaches is crucial. This study investigated a machine-learning-based light gradient boosting machine (LightGBM) framework for predicting estuarine water levels using historical water levels as the only inputs. Two prediction models based on the LightGBM framework, denoted as LightGBM1 and LightGBM2, are developed. The LightGBM1 model constructs only a single regression model and uses a recursive approach to generate multidimensional outputs. The LightGBM2 model constructs multiple regression models between the same inputs and outputs in each dimension. The LightGBM1 and LightGBM2 models were applied to the Yangtze estuary as a test case. The results demonstrate that both models are effective at predicting short-term (within 48 hours) estuarine water levels, but the statistical performance of LightGBM2 is better overall. For 24-hour prediction, the root-mean-squared errors of the LightGBM1 and LightGBM2 models are in the ranges of 0.14–0.17 m and 0.12–0.15 m, respectively.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Additional Information:	License information from Publisher: LICENSE 1: Title: This article is under embargo with an end date yet to be finalised.
Publisher:	Elsevier
ISSN:	1463-5003
Date of First Compliant Deposit:	9 May 2024
Date of Acceptance:	5 May 2024
Last Modified:	10 Nov 2024 03:00
URI:	https://orca.cardiff.ac.uk/id/eprint/168803

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