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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 4
Hydrol. Earth Syst. Sci., 20, 1405–1412, 2016
https://doi.org/10.5194/hess-20-1405-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 20, 1405–1412, 2016
https://doi.org/10.5194/hess-20-1405-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Technical note 14 Apr 2016

Technical note | 14 Apr 2016

Technical note: Application of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest

Yabin Sun et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (12 Dec 2015) by Dimitri Solomatine
AR by Svenja Lange on behalf of the Authors (07 Jan 2016)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (19 Jan 2016) by Dimitri Solomatine
RR by Anonymous Referee #1 (13 Feb 2016)
RR by Anonymous Referee #2 (20 Feb 2016)
ED: Publish subject to minor revisions (Editor review) (14 Mar 2016) by Dimitri Solomatine
AR by Anna Wenzel on behalf of the Authors (21 Mar 2016)  Author's response    Manuscript
ED: Publish as is (23 Mar 2016) by Dimitri Solomatine
Publications Copernicus
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Short summary
This study applies artificial neural networks (ANN) to predict the groundwater table variations in a tropical wetland in Singapore. Surrounding reservoir levels and rainfall are selected as ANN inputs. The limited number of inputs eliminates the data-demanding restrictions inherent in the physical-based numerical models. The forecast is made at 4 locations with 3 leading times up to 7 days. The ANN forecast shows promising accuracy with decreasing performance when leading time progresses.
This study applies artificial neural networks (ANN) to predict the groundwater table variations...
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