增强环境数据归算:一个物理限制的机器学习框架
Marcos Pastorini1, Rafael Rodríguez2, Lorena Etcheverry1
1Department of Computer Science, School of Engineering, Universidad de la República, Herreira y Reissig, 565, Montevideo 11300, Uruguay.
The Science of the total environment
|March 24, 2024
概括
本研究引入了一种机器学习框架,以填补缺少的环境数据,提高流域模型的准确性. 该方法有效地归因于气象,水量和质量数据,减少水资源管理中的不确定性.
科学领域:
- 环境科学 环境科学
- 管理水资源 管理水资源
- 机器学习 机器学习
背景情况:
- 综合流域模型对于分析气候和水资源至关重要.
- 有限的现场数据给这些模型带来了显著的不确定性.
- 在收集更多数据之前,利用现有数据是改善模型性能的关键.
研究的目的:
- 开发一种新的机器学习框架,用于归因缺少的环境数据.
- 评估框架在处理不同领域数据缺口方面的有效性.
- 通过数据增强来提高综合环境模型的性能.
主要方法:
- 开发了一个包含物理约束的机器学习框架.
- 该框架被应用在气象学,水量和水质方面的缺失数据上.
- 模型性能使用纳什-萨特克利夫效率 (NSE) 度量来评估.
主要成果:
- 该框架成功地将环境领域中高比例的缺失数据归因于环境领域.
- 获得了令人满意的归算结果,气象学最低NSE值为0.72,水文学变量为>0.97.
- 超过78%的物理水质变量和66%的化学水质变量分别显示NSE>0.45和>0.35.
结论:
- 拟议的机器学习框架对于环境建模中的数据增强是有效的.
- 输入缺失数据显著提高了性能,并减少了综合流域模型中的不确定性.
- 这种方法提供了一个有价值的工具,通过增强数据可用性来优化水资源管理.
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