Articles | Volume 20, issue 4
https://doi.org/10.5194/hess-20-1405-2016
https://doi.org/10.5194/hess-20-1405-2016
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, Dadiyorto Wendi, Dong Eon Kim, and Shie-Yui Liong

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

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 Yabin Sun on behalf of the Authors (30 Dec 2015)
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 Yabin Sun on behalf of the Authors (19 Mar 2016)
ED: Publish as is (23 Mar 2016) by Dimitri Solomatine
AR by Yabin Sun on behalf of the Authors (06 Apr 2016)
Download
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.