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Reference: Colchester, F, Marais, HG, Thomson, P et al., (2017). Accidental infrastructure for groundwater monitoring in Africa. Environmental Modelling and Software, 91, 241-250.Citable link to this page:

 

Accidental infrastructure for groundwater monitoring in Africa

Abstract: A data deficit in shallow groundwater monitoring in Africa exists despite one million handpumps being used by 200 million people every day. Recent advances with “smart handpumps” have provided accelerometry data sent automatically by SMS from transmitters inserted in handles to estimate hourly water usage. Exploiting the high-frequency “noise” in handpump accelerometry data, we model high-rate wave forms using robust machine learning techniques sensitive to the subtle interaction between pumping action and groundwater depth. We compare three methods for representing accelerometry data (wavelets, splines, Gaussian processes) with two systems for estimating groundwater depth (support vector regression, Gaussian process regression), and apply three systems to evaluate the results (held-out periods, held-out recordings, balanced datasets). Results indicate that the method using splines and support vector regression provides the lowest overall errors. We discuss further testing and the potential of using Africas accidental infrastructure to harmonise groundwater monitoring systems with rural water-security goals.

Publication status:PublishedPeer Review status:Peer reviewedVersion:Publisher's VersionDate of acceptance:2017-01-27 Funder: NERC/ESRC/DFID’s UPGro program   Funder: ESRC/DFID   Funder: John Fell Fund   Funder: Department for International Development   Notes:© 2017 Colchester, et al. Published by Elsevier Ltd. This is an open access article published under a Creative Commons license, see: https://creativecommons.org/licenses/by/4.0/

Bibliographic Details

Publisher: Elsevier

Publisher Website: https://www.elsevier.com/

Journal: Environmental Modelling and Softwaresee more from them

Publication Website: http://www.sciencedirect.com/science/journal/13648152

Volume: 91

Issue Date: 2017-02-20

pages:241-250Identifiers

Doi: https://doi.org/10.1016/j.envsoft.2017.01.026

Issn: 1364-8152

Eissn: 1873-6726

Uuid: uuid:e197e110-9e41-4219-bfa4-7f050ebfdf7d

Urn: uri:e197e110-9e41-4219-bfa4-7f050ebfdf7d

Pubs-id: pubs:682298 Item Description

Type: journal-article;

Version: Publisher's VersionKeywords: Journal Article

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Author: Colchester, F - Oxford, MPLS, Engineering Science University College fundingEngineering and Physical Sciences Research Council -

Source: https://ora.ox.ac.uk/objects/uuid:e197e110-9e41-4219-bfa4-7f050ebfdf7d



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