Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis |
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Authors: | Ahmad Sharafati Seyed Babak Haji Seyed Asadollah Davide Motta |
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Institution: | 1. Institute of Research and Development, Duy Tan University , Da Nang 550000, Vietnam;2. Faculty of Civil Engineering, Duy Tan University , Da Nang 550000, Vietnam;3. Department of Civil Engineering, Science and Research Branch, Islamic Azad University , Tehran, Iran ahmadsharafati@duytan.edu.vn asharafati@gmail.comhttps://orcid.org/0000-0003-0448-2871;5. Department of Civil Engineering, Science and Research Branch, Islamic Azad University , Tehran, Iran;6. Department of Mechanical and Construction Engineering, Northumbria University , Newcastle upon Tyne, UK |
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Abstract: | 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. |
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Keywords: | suspended sediment load ensemble machine learning prediction uncertainty analysis |
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