Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning,Hydrological Processes

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Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning
Hydrological Processes ( IF 2.8 ) Pub Date : 2020-12-03 , DOI: 10.1002/hyp.14000
Mark B. Green _{1,

2} , Linda H. Pardo ₂ , Scott W. Bailey ₂ , John L. Campbell ₂ , William H. McDowell ₃ , Emily S. Bernhardt ₄ , Emma J. Rosi ₅

Affiliation

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.

中文翻译：

使用水质传感器和机器学习预测河流溶质浓度的高频变化

溪流溶质监测已对生态系统和地球系统功能产生了许多见解。尽管新的传感器提供了有关某些溪流水溶质的精细尺度时间变化的新颖信息，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–来预测15种溶液中15种溶质的浓度，其中有8种缺乏特定的传感器。在美国新罕布什尔州的哈伯德布鲁克实验森林内的水文参考流中，使用密集流感测和手动流采样（每周一次）数据进行了整整四年的算法训练。随机森林算法在预测溶质浓度方面比支持向量机算法稍好（随机森林的纳什-萨特克利夫效率范围为0.35至0.78，而支持向量机的纳什-萨特克利夫效率范围为0.29至0.79）。溶质预测对两种算法的独立变量-荧光溶解的有机物，pH值和比电导的去除最敏感，对溶解氧和浊度最不敏感。钙和单体铝的预测浓度用于估算集水区溶质的收率，铝的流变最大，因为铝随着流的排放而浓缩。

更新日期：2021-01-19

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