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基于综合机器学习模型的水质预测方法:对达林河流域的一个案例研究
1College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning Province, China.
Journal of contaminant hydrology
|September 17, 2025
概括
河流中的总 (TN) 含量因季节性变化而波动,冬季达到峰值,夏季达到最低点. 一个先进的ELSTM-EBP模型准确地预测了这些变化,并确定了改善水质管理的关键影响因素.
科学领域:
- 环境科学 环境科学
- 水质管理水质管理
- 机器学习应用 机器学习应用
背景情况:
- 总 (TN) 是影响河水质量的关键污染物.
- 了解TN的时空动态和驱动因素对于污染控制至关重要.
- 现有的方法在实时监控,时空建模和多源数据分析方面面临挑战.
研究的目的:
- 分析在达林河流域的TN度的时空变化.
- 确定影响TN水平的关键因素.
- 开发和验证用于TN预测的准确机器学习模型.
主要方法:
- 综合时空加权插值,特征选择和时间序列分解.
- 开发了一种增强的长期短期记忆与反向传播网络 (ELSTM-EBP) 模型.
- 利用了来自达林河流域 (2023-2025) 七个监测站的数据.
主要成果:
- 度表现出"U"形的年度波动,在冬季 (一月至二月) 最高,在八月最低.
- 在监测点之间观察到TN水平的显著空间异质性.
- 水温,溶解氧气,酸盐指数和度被确定为重要的影响因素.
结论:
- 与其他模型相比,ELSTM-EBP模型在TN预测方面表现出卓越的准确性和概括能力.
- 该模型显示了强大的短期预测性能,精度略有下降,最高可达7步.
- 调查结果为有效的水质风险评估和减缓策略提供了基础.
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