Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Sharafati, Ahmad, Haji Seyed Asadollah, Seyed Babak, Motta, Davide and Yaseen, Zaher Mundher (2020) Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis. Hydrological Sciences Journal, 65 (12). pp. 2022-2042. ISSN 0262-6667

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Official URL: https://doi.org/10.1080/02626667.2020.1786571

Abstract

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.

Item Type: Article
Uncontrolled Keywords: suspended sediment load, ensemble machine learning, prediction, uncertainty analysis
Subjects: F900 Others in Physical Sciences
H200 Civil Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Rachel Branson
Date Deposited: 19 Nov 2021 16:29
Last Modified: 19 Nov 2021 16:30
URI: http://nrl.northumbria.ac.uk/id/eprint/47802

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