Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-55-2022
https://doi.org/10.5194/hess-26-55-2022
Research article
 | 
06 Jan 2022
Research article |  | 06 Jan 2022

Using machine learning to predict optimal electromagnetic induction instrument configurations for characterizing the shallow subsurface

Kim Madsen van't Veen, Ty Paul Andrew Ferré, Bo Vangsø Iversen, and Christen Duus Børgesen

Related authors

Improving understanding of groundwater flow in an alpine karst system by reconstructing its geologic history using conduit network model ensembles
Chloé Fandel, Ty Ferré, François Miville, Philippe Renard, and Nico Goldscheider
Hydrol. Earth Syst. Sci., 27, 4205–4215, https://doi.org/10.5194/hess-27-4205-2023,https://doi.org/10.5194/hess-27-4205-2023, 2023
Short summary
Reproducibility of the wet part of the soil water retention curve: a European interlaboratory comparison
Benjamin Guillaume, Hanane Aroui Boukbida, Gerben Bakker, Andrzej Bieganowski, Yves Brostaux, Wim Cornelis, Wolfgang Durner, Christian Hartmann, Bo V. Iversen, Mathieu Javaux, Joachim Ingwersen, Krzysztof Lamorski, Axel Lamparter, András Makó, Ana María Mingot Soriano, Ingmar Messing, Attila Nemes, Alexandre Pomes-Bordedebat, Martine van der Ploeg, Tobias Karl David Weber, Lutz Weihermüller, Joost Wellens, and Aurore Degré
SOIL, 9, 365–379, https://doi.org/10.5194/soil-9-365-2023,https://doi.org/10.5194/soil-9-365-2023, 2023
Short summary
Are maps of nitrate reduction in groundwater altered by climate and land use changes?
Ida Karlsson Seidenfaden, Torben Obel Sonnenborg, Jens Christian Refsgaard, Christen Duus Børgesen, Jørgen Eivind Olesen, and Dennis Trolle
Hydrol. Earth Syst. Sci., 26, 955–973, https://doi.org/10.5194/hess-26-955-2022,https://doi.org/10.5194/hess-26-955-2022, 2022
Short summary
Tandem use of transit time distribution and fraction of young water reveals the dynamic flow paths supporting streamflow at a mountain headwater catchment
Ravindra Dwivedi, Christopher Eastoe, John F. Knowles, Jennifer McIntosh, Thomas Meixner, Ty P. A. Ferre, Rebecca Minor, Greg Barron-Gafford, Nathan Abramson, Michael Stanley, and Jon Chorover
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-355,https://doi.org/10.5194/hess-2021-355, 2021
Manuscript not accepted for further review
Short summary
A stepwise GIS approach for the delineation of river valley bottom within drainage basins using a cost distance accumulation analysis
Gasper L. Sechu, Bertel Nilsson, Bo V. Iversen, Mette B. Greve, Christen D. Børgesen, and Mogens H. Greve
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-361,https://doi.org/10.5194/hess-2020-361, 2020
Manuscript not accepted for further review

Related subject area

Subject: Vadose Zone Hydrology | Techniques and Approaches: Modelling approaches
Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data
Lukas Strebel, Heye Bogena, Harry Vereecken, Mie Andreasen, Sergio Aranda-Barranco, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 28, 1001–1026, https://doi.org/10.5194/hess-28-1001-2024,https://doi.org/10.5194/hess-28-1001-2024, 2024
Short summary
A comprehensive study of deep learning for soil moisture prediction
Yanling Wang, Liangsheng Shi, Yaan Hu, Xiaolong Hu, Wenxiang Song, and Lijun Wang
Hydrol. Earth Syst. Sci., 28, 917–943, https://doi.org/10.5194/hess-28-917-2024,https://doi.org/10.5194/hess-28-917-2024, 2024
Short summary
Modelling groundwater recharge, actual evaporation, and transpiration in semi-arid sites of the Lake Chad basin: the role of soil and vegetation in groundwater recharge
Christoph Neukum, Angela Morales-Santos, Melanie Ronelngar, Aminu Bala, and Sara Vassolo
Hydrol. Earth Syst. Sci., 27, 3601–3619, https://doi.org/10.5194/hess-27-3601-2023,https://doi.org/10.5194/hess-27-3601-2023, 2023
Short summary
Predicting soil hydraulic properties for binary mixtures – concept and application for constructed Technosols
Moreen Willaredt, Thomas Nehls, and Andre Peters
Hydrol. Earth Syst. Sci., 27, 3125–3142, https://doi.org/10.5194/hess-27-3125-2023,https://doi.org/10.5194/hess-27-3125-2023, 2023
Short summary
Identification of Parameter Importance for Benzene Transport in the Unsaturated Zone Using Global Sensitivity Analysis
Meirav Cohen, Nimrod Schwartz, and Ravid Rosenzweig
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-183,https://doi.org/10.5194/hess-2023-183, 2023
Revised manuscript accepted for HESS
Short summary

Cited articles

Acworth, R. I.: Investigation of dryland salinity using the electrical image method, Aust. J. Soil Res., 37, 623–636, https://doi.org/10.1071/sr98084, 1999. 
Adamchuk, V., Allred, B., Doolittle, J., Grote, K., and Rossel, R. V.: Tools for proximal soil sensing [Supplement to Chapter 4], in: Soil Survey Division Staff, 1993, Soil survey manual, US Department of Agriculture Handbook 18, Soil Conservation Service, available at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcseprd329418 (last access: 15 July 2020), 2015. 
Adhikari, K. and Hartemink, A. E.: Soil organic carbon increases under intensive agriculture in the Central Sands, Wisconsin, USA, Geoderma Regional, 10, 115–125, https://doi.org/10.1016/j.geodrs.2017.07.003, 2017. 
Anderson, W. L.: Numerical integration of related Hankel transforms of orders 0 and 1 by adaptive digital filtering, Geophysics, 44, 1287–1305, 1979. 
Auken, E., Christiansen, A. V., Kirkegaard, C., Fiandaca, G., Schamper, C., Behroozmand, A. A., Binley, A., Nielsen, E., Effersø, F., Christensen, N. B., Sørensen, K., Foged, N., and Vignoli, G.: An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data, Explor. Geophys., 46, 223–235, https://doi.org/10.1071/EG13097, 2015. 
Download
Short summary
Geophysical instruments are often used in hydrological surveys. A geophysical model that couples electrical conductivity in the subsurface layers with measurements from an electromagnetic induction instrument was combined with a machine learning algorithm. The study reveals that this combination can estimate the identifiability of electrical conductivity in a layered soil and provide insight into the best way to configure the instrument for a specific field site.