在黄河盆地基于机器学习的溶氧预测的比较分析:各种环境预测因素的作用
Lingling Liu1, Xiaoli Zhao1, Lingfeng Zhou2
1College of Water Sciences, Beijing Normal University, Beijing, 100875, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
Journal of environmental management
|September 9, 2025
概括
捕获属性有效地预测黄河流域的河流溶解氧 (DO) 动态,通常单独超过水质数据. 结合这两种预测类型,进一步提高了河流健康管理的模型准确性.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 机器学习应用 机器学习应用
背景情况:
- 溶解氧 (DO) 是河流健康的关键指标,需要精确的时空建模来有效管理.
- 机器学习 (ML) 模型对水质预测有希望,但它们的性能取决于预测变量选择.
- 预测数据的可用性,特别是流域属性与直接水质测量相比,差异很大.
研究的目的:
- 用随机森林 (RF) 模型评估易于访问的水域属性和气象数据 (CAM) 与水质参数 (WQPs) 在预测黄河流域 (YRB) 河流DO动态方面的有效性.
- 确定是否需要结合CAM和WQP数据来提高DO预测准确度.
- 评估是否需要收集额外的水质数据,以加强河流健康监测.
主要方法:
- 在2016年至2022年期间,从YRB的135个监测站点收集了大约10,800个月的DO测量结果.
- 开发并比较使用三个预测变量集的射频模型:CAM,WQPs和CAM + WQP数据集的组合.
- 通过使用Nash-Sutcliffe效率 (NSE) 在不同地点和条件上评估模型性能.
主要成果:
- 射频模型实现了令人满意的性能,CAM,WQP和CAM + WQP模型的NSE>0.35分别在67%,61%和73%的场地.
- 在一般情况下,CAM模型的表现优于WQP模型,这很可能是因为它包含了核心WQP变量和额外的环境因素,如大气压.
- 在具有高度人为影响和水资源管理活动的地区,WQP模型被证明比单独的CAM更有效,而CAM + WQP组合模型显示出最佳的整体性能.
结论:
- 随时可用的流域属性和气象数据可以有效地预测像YRB这样的大流域的河流DO动态,往往超过单独的水质参数的预测能力.
- 虽然CAM数据提供了可靠的基线,但结合特定的水质参数对于在人类影响地区准确建模DO至关重要.
- 综合CAM + WQP方法提供了最全面的DO预测框架,尽管在预测某些地区的极端值和稀疏数据方面存在局限性,指导未来的监测和建模策略.
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