Toward Trustworthy Machine Learning for Daily Sediment Modeling in the Riverine Systems: An Integrated Framework With Enhanced Uncertainty Quantification and Interpretability

Toward Trustworthy Machine Learning for Daily Sediment Modeling in the Riverine Systems: An Integrated Framework With Enhanced Uncertainty Quantification and Interpretability

Abstract Accurately predicting sediment dynamics and understanding their intrinsic contributors are pivotal for sustainable environment and water management. While machine learning (ML) enables precise predictions, its “black‐box” nature hinders transparency and credibility, posing challenges in int...

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Bibliographic Details
Main Authors: Z. J. Yue, N. N. Wang, B. D. Xu, X. Huang, D. M. Yang, H. B. Xiao, Z. H. Shi
Format: Article
Language:English
Published: Wiley 2025-05-01
Series:Water Resources Research
Subjects:
suspended sediment concentration
machine learning
model interpretability
uncertainty quantification
conformal prediction
Online Access:https://doi.org/10.1029/2024WR038650
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https://doi.org/10.1029/2024WR038650

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