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Supervised learning-based water quality prediction and ecological risk factor mining across China’s 12 major river basins
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DOI：10.1016/j.envsoft.2025.106840.png)
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Abstract
En 中文
• New method (spatio–temporal SANN). We propose a spatio-temporal aware neural network (SANN) for water quality prediction. • New perspective (interpretable drivers). We use a gradient-based attribution method to quantify the effects of indicators across river basins. • New application setting (12-basin evaluation). We construct a benchmark dataset covering China’s 12 major river basins and conduct water quality prediction tasks on this dataset. • Empirical gains (accuracy and robustness). Extensive water quality prediction experiments across 12 river basins show that SANN outperforms strong baselines.
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