Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO

Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO

Abstract Streambed grain sizes control river hydro‐biogeochemical (HBGC) processes and functions. However, measuring their quantities, distributions, and uncertainties is challenging due to the diversity and heterogeneity of natural streams. This work presents a photo‐driven, artificial intelligence...

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Bibliographic Details
Main Authors: Yunxiang Chen, Jie Bao, Rongyao Chen, Bing Li, Yuan Yang, Lupita Renteria, Dillman Delgado, Brieanne Forbes, Amy E. Goldman, Manasi Simhan, Morgan E. Barnes, Maggi Laan, Sophia McKever, Z. Jason Hou, Xingyuan Chen, Timothy Scheibe, James Stegen
Format: Article
Language:English
Published: Wiley 2024-11-01
Series:Water Resources Research
Subjects:
streambed
grain size quantification
artificial intelligence
uncertainty quantification
smartphone photos
hydro‐biogeochemistry
Online Access:https://doi.org/10.1029/2023WR036456
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https://doi.org/10.1029/2023WR036456

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