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Yingna Sun

As seen in: MDPI, Taylor & Francis Online
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Articles

Hydrological drought projection in the Lower Songhua River basin under climate and land-use change

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By Yingna Sun, Tao Liu, Yusu Zhao
| Taylor & Francis Online
Advanced search Hydrological Sciences Journal Latest Articles Submit an article Journal homepage Full Article Figures & data References Citations Metrics Reprints & Permissions Read this article /doi/full/10.1080/02626667.2026.2671903?needAccess=true ABSTRACT Climate change and land-use change exacerbate risks of hydrological drought.
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An Architecture-Feature-Enhanced Decision Framework for Deep Learning-Based Prediction of Extreme and Imbalanced Precipitation

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By Yingna Sun, Zhicheng Yue, Zhinan Li, WenJiu Yu
| preprints.org
Submitted: 03 December 2025 You are already at the latest version Accurate precipitation prediction is critical for water security and disaster mitigation, yet remains challenging due to atmospheric complexity and class imbalance in rainfall data. This study introduces an integrated "architecture-feature-augmentation" framework to address these limitations.
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The Analysis of Present and Future Use of Non-Conventional Water Resources in Heilongjiang Province, China

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By Hongcong Guo, Yingna Sun, Tienan Li, Yun Teng
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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