Premium
Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning
Author(s) -
Green Mark B.,
Pardo Linda H.,
Bailey Scott W.,
Campbell John L.,
McDowell William H.,
Bernhardt Emily S.,
Rosi Emma J.
Publication year - 2021
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.14000
Subject(s) - random forest , water quality , support vector machine , environmental science , dissolved organic carbon , sampling (signal processing) , hydrology (agriculture) , algorithm , computer science , machine learning , chemistry , geology , environmental chemistry , ecology , geotechnical engineering , filter (signal processing) , computer vision , biology
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.