Next Article in Journal
Model-Driven Strategies for Sulfide Control in a Regional Wastewater System Receiving Tannery Effluents in Portugal
Previous Article in Journal
Impact of River Damming on Downstream Hydrology and Hydrochemistry: The Case of Lower Nestos River Catchment (NE. Greece)
Article

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 / Revised: 25 September 2021 / Accepted: 2 October 2021 / 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. View Full-Text
Keywords: machine learning; groundwater-surface water interactions; integrated hydrologic models; groundwater recharge; groundwater monitoring machine learning; groundwater-surface water interactions; integrated hydrologic models; groundwater recharge; groundwater monitoring
Show Figures

Figure 1

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 Style

Moghaddam, 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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop