使用输入变量选择技术分析溶解氧影响因素和度预测:混合机器学习方法
Wei Liu1, Shu Lin2, Xiaobao Li2
1School of Environment and Energy, Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Resource Recycling, South China University of Technology, Guangzhou, 510006, China.
Journal of environmental management
|April 6, 2024
概括
在水生生态系统中,准确的溶氧 (DO) 预测至关重要. 一个新的MIC-SVR模型有效地识别了关键的环境因素,并提高了在坦江河中DO度预测的准确性.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 生态系统监测 生态系统监测
背景情况:
- 精确的溶氧量化对于水生生态系统的健康至关重要.
- 现有的机械模型是复杂的,更不便携.
- 数据驱动模型面临着许多输入变量带来的挑战,影响速度和性能.
研究的目的:
- 开发一个有效的模型来预测唐江河的DO度.
- 确定影响DO变化的主要环境因素.
- 为了比较支持向量回归 (SVR) 和最大信息系数增强的SVR (MIC-SVR) 模型的性能.
主要方法:
- 利用了来自坦江河的水质和气象数据.
- 使用最大信息系数 (MIC) 进行输入变量选择.
- 开发并比较SVR和MIC-SVR模型用于DO度估计.
主要成果:
- 确定了主要污染因子从氨到总的转变.
- 与SVR相比,MIC-SVR模型显示RMSE减少了4.46%和NSE改善了45.85%.
- 优化内核功能选择对于提高SVR模型性能至关重要.
结论:
- MIC-SVR模型是分析DO环境因素关系的有效工具.
- 该模型准确地预测了唐江河的DO度,特别是在中流和下流地区.
- 调查结果为水环境保护和资源管理决策提供了宝贵的见解.
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