Prediction of natural runoff in China based on multi-scenario climate models with self-attention neural networks.

Clicks: 58
ID: 282882
2025
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #165 of 168 articles by views in Water research

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 168 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Climate change is increasingly affecting the global water cycle. Developing high-quality climate-runoff relationship models can help assess its impact on surface natural runoff, thereby enhancing resilience to water resource risks. This study extends the applicability paradigm of self-attention mechanisms to non-sequential hydrological modeling by developing a Self-Attention Artificial Neural Network (SAANN), which quantifies climate change impacts on China's natural runoff. The model was trained with climate, underlying surface, and natural runoff data from China (2000-2018), utilizing Bayesian optimization for hyperparameter tuning. Compared to the ANN, SAANN's mean square error (MSE) in the test set is reduced by 26.9 %, demonstrating its superior prediction performance. SAANN visualizes the attention relationships between input variables in the model's attention layer, highlighting key correlations similar to physical model principles. This improves both model interpretability and output reliability. Based on the methodology developed above, the study predicts natural runoff in China for the near-term (2041-2050), mid-term (2061-2070), and long-term (2091-2100) under two emission scenarios (SSP245 and SSP585), and examines the driving factors. The results show that natural runoff in China is expected to increase under various future scenarios. Notably, the increase rate under the SSP585 scenario is significantly higher than that under SSP245. The autumn increase is particularly pronounced compared to other seasons, and the northern basin generally experiences a higher increase rate than the southern basin. These changes may pose new adaptive challenges for agricultural production and water conservancy in China.
Reference Key
hu2025prediction Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hu, Naixin; Shu, Kai; Zhang, Yuezheng; Alfonso, Leonardo; Li, Chenyang; Zheng, Tong
Journal Water research
Year 2025
DOI
10.1016/j.watres.2025.123768
URL
Keywords
climate change interpretability multi-scenario prediction natural runoff self-attention neural networks

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.