Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.153
IF5.153
IF 5-year value: 5.460
IF 5-year
5.460
CiteScore value: 7.8
CiteScore
7.8
SNIP value: 1.623
SNIP1.623
IPP value: 4.91
IPP4.91
SJR value: 2.092
SJR2.092
Scimago H <br class='widget-line-break'>index value: 123
Scimago H
index
123
h5-index value: 65
h5-index65
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.

Viewed

Total article views: 1,565 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
879 622 64 1,565 74 71
  • HTML: 879
  • PDF: 622
  • XML: 64
  • Total: 1,565
  • BibTeX: 74
  • EndNote: 71
Views and downloads (calculated since 10 Sep 2015)
Cumulative views and downloads (calculated since 10 Sep 2015)

Cited

Saved (final revised paper)

Saved (preprint)

No saved metrics found.

Discussed (final revised paper)

No discussed metrics found.

Discussed (preprint)

No discussed metrics found.
Latest update: 26 Nov 2020
Publications Copernicus
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.
This study applies artificial neural networks (ANN) to predict the groundwater table variations...
Citation