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

Ahmad Sharafati*, Seyed Babak Haji Seyed Asadollah, Davide Motta, Zaher Mundher Yaseen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)2022-2042
Number of pages21
JournalHydrological Sciences Journal
Volume65
Issue number12
Early online date15 Jul 2020
DOIs
Publication statusPublished - 9 Sep 2020

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