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河流水质预测:通过多来源数据增强的新型LSTM-变压器方法
Juan Huan1, Chen Zhang2, Xiangen Xu3
1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, 213164, China. huanjuan@cczu.edu.cn.
Environmental monitoring and assessment
|August 26, 2025
概括
一个新的深度学习模型准确地预测了北京 - 杭州运河的总 (TP) 和总 (TN). 这种先进的水质预测支持有效的水资源管理和生态保护.
科学领域:
- 环境科学
- 水资源管理
- 数据科学
背景情况:
- 有效的水质预测对于管理水资源和保护生态系统至关重要.
- 北京 - 杭州运河的缩需要先进的监测和控制策略.
研究的目的:
- 开发一个深度学习模型来预测总 (TP) 和总 (TN) 度.
- 提高北京 - 杭州运河州部分水质预测的准确性.
主要方法:
- 开发了一种混合波形消噪 (WD) -LSTM-变压器模型.
- 该模型整合了水质数据,土地使用信息和气象因素.
- 使用SHAP方法进行模型解释性和变量显著性分析.
主要成果:
- WD-LSTM-变压器模型实现了TP和TN的高预测精度 (R2 > 0.9).
- 与四种传统预测模型相比,该模型表现出更高的性能.
- 确定了影响TP和TN波动的重要变量.
结论:
- 提出的模型为预测水质提供了可靠和可解释的方法.
- 这项研究为识别污染源和改善流域管理提供了科学基础.
- 这些发现支持在关键水体中预防和控制肥胖的努力.
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