Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux?
1
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ 85721, USA
2
Pacific Northwest National Laboratory, Richland, WA 99354, USA
3
Sandia National Laboratory, Albuquerque, NM 87123, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Achim A. Beylich
Water 2021, 13(20), 2837; https://doi.org/10.3390/w13202837
Received: 20 July 2021
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Revised: 25 September 2021
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Accepted: 2 October 2021
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Published: 12 October 2021
(This article belongs to the Section Hydrogeology)
Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical and numerical models to temperature time series measured at multiple depths to infer daily average flux. These methods have seen wide use in hydrologic science despite strong simplifying assumptions including a lack of consideration of model structural error or the impacts of multidimensional flow or the impacts of transient streambed hydraulic properties. We performed a “perfect-model experiment” investigation to examine whether regression trees, with and without gradient boosting, can extract sufficient information from model-generated subsurface temperature time series, with and without added measurement error, to infer the corresponding exchange flux time series at the streambed surface. Using model-generated, synthetic data allowed us to assess the basic limitations to the use of machine learning; further examination of real data is only warranted if the method can be shown to perform well under these ideal conditions. We also examined whether the inherent feature importance analyses of tree-based machine learning methods can be used to optimize monitoring networks for exchange flux inference.
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Keywords:
machine learning; groundwater-surface water interactions; integrated hydrologic models; groundwater recharge; groundwater monitoring
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MDPI and ACS Style
Moghaddam, M.A.; Ferré, T.P.A.; Chen, X.; Chen, K.; Song, X.; Hammond, G. Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux? Water 2021, 13, 2837. https://doi.org/10.3390/w13202837
AMA Style
Moghaddam MA, Ferré TPA, Chen X, Chen K, Song X, Hammond G. Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux? Water. 2021; 13(20):2837. https://doi.org/10.3390/w13202837
Chicago/Turabian StyleMoghaddam, Mohammad A., Ty P.A. Ferré, Xingyuan Chen, Kewei Chen, Xuehang Song, and Glenn Hammond. 2021. "Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux?" Water 13, no. 20: 2837. https://doi.org/10.3390/w13202837
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