Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 4
Articles | Volume 30, issue 4
https://doi.org/10.5194/hess-30-1077-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-1077-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 4
Research article
 | 
24 Feb 2026
Research article |  | 24 Feb 2026

Sensitivity of hydrological machine learning prediction accuracy to information quantity and quality

Minhyuk Jeung, Younggu Her, Sang-Soo Baek, and Kwangsik Yoon

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Short summary
Machine learning (ML) techniques have become widely used due to the availability of large data repositories and advancements in computing resources and methods. Our study explored the connection between a model’s accuracy and the information content of input data. Results showed that the accuracy of three ML models significantly improved when high-quality input data were included. These findings highlight the importance of data quality in ML model training.
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