Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning
Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning
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DOI：
10.1002/hyp.14000
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发表时间：
2020-12
影响因子：
3.2
通讯作者：
M. Green;L. Pardo;S. Bailey;J. Campbell;W. McDowell;E. Bernhardt;E. Rosi
中科院分区：
文献类型：
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作者：
M. Green;L. Pardo;S. Bailey;J. Campbell;W. McDowell;E. Bernhardt;E. Rosi
河流溶质监测对生态系统和地球系统功能产生了许多见解。虽然新的传感器已经提供了关于一些溪流溶质的细尺度时间变化的新信息，但我们缺乏足够的传感器技术来获得许多其他溶质的相同见解。我们使用了两种机器学习算法——支持向量机和随机森林——以15分钟的分辨率预测10种溶质的浓度，其中8种缺乏特定的传感器。这些算法在美国新罕布什尔州哈伯德溪实验森林的水文参考河流中进行了为期四年的密集溪流传感和人工溪流采样（每周）数据训练。随机森林算法在预测溶质浓度方面略优于支持向量机算法（随机森林的Nash - Sutcliffe效率范围为0.35 - 0.78，而支持向量机为0.29 - 0.79）。溶质预测对荧光溶解有机物的去除最敏感，pH值和特定电导作为两种算法的独立变量，对溶解氧和浊度最不敏感。预测的钙和单体铝的浓度被用来估计流域溶质产量，铝的溶质产量变化最大，因为它随着河流排放而浓缩。这些结果显示了使用流传感和密集流离散采样相结合的方法来建立有关溶质高频变化的信息的巨大希望，而适当的传感器或代理无法获得。
Stream solute monitoring has produced many insights into ecosystem and Earth system functions. Although new sensors have provided novel information about the fine‐scale temporal variation of some stream water solutes, we lack adequate sensor technology to gain the same insights for many other solutes. We used two machine learning algorithms – Support Vector Machine and Random Forest – to predict concentrations at 15‐min resolution for 10 solutes, of which eight lack specific sensors. The algorithms were trained with data from intensive stream sensing and manual stream sampling (weekly) for four full years in a hydrologic reference stream within the Hubbard Brook Experimental Forest in New Hampshire, USA. The Random Forest algorithm was slightly better at predicting solute concentrations than the Support Vector Machine algorithm (Nash‐Sutcliffe efficiencies ranged from 0.35 to 0.78 for Random Forest compared to 0.29 to 0.79 for Support Vector Machine). Solute predictions were most sensitive to the removal of fluorescent dissolved organic matter, pH and specific conductance as independent variables for both algorithms, and least sensitive to dissolved oxygen and turbidity. The predicted concentrations of calcium and monomeric aluminium were used to estimate catchment solute yield, which changed most dramatically for aluminium because it concentrates with stream discharge. These results show great promise for using a combined approach of stream sensing and intensive stream discrete sampling to build information about the high‐frequency variation of solutes for which an appropriate sensor or proxy is not available.