Developing ensemble models for estimating sediment loads for different times scales

Majid Niazkar () and Mohammad Zakwan ()
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Majid Niazkar: Free University of Bozen-Bolzano
Mohammad Zakwan: MANUU

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 6, No 77, 15557-15575

Abstract: Abstract Sediment ratings supply an important input to the design of water resources projects. Nevertheless, the accuracy of sediment ratings has remained a matter of concern for hydrologists. The present article investigates both the aspect of improving the accuracy, i.e., modifying the simple rating curve equation by introducing a four-parameter equation and application of ensemble machine learning (ML) and ensemble empirical models, to estimate sediment loads. The ML models include artificial neural networks, multi-gene genetic programming (MGGP), and a hybrid MGGP-based model. Published field data at two measuring stations were used to assess the performance of different models employed in this study. The comparative analysis conducted in this study provides a novel comparison of sediment load estimations for three time scales. For instance, the ML-based simple average ensemble model (i.e., 556.5, 255.0, and 0.759) and the empirical-based nonlinear ensemble model (i.e., 549.1, 378.6, and 0.589) achieved the lowest root-mean-square errors and mean absolute errors and highest determination coefficients for the train and test monthly sediment data of the first station, respectively. Finally, the findings demonstrate that ensemble-based models generally improve the estimates of sediment loads at daily, 10-daily, and monthly scales.

Keywords: Sediment load; Rivers; Ensemble model; Machine learning; Sediment rating curve (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10668-023-03263-4

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