Articles | Volume 20, issue 4
https://doi.org/10.5194/hess-20-1405-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-1405-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Technical note: Application of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest
Tropical Marine Science Institute, National University of Singapore,
Singapore
Dadiyorto Wendi
Tropical Marine Science Institute, National University of Singapore,
Singapore
Dong Eon Kim
Tropical Marine Science Institute, National University of Singapore,
Singapore
Shie-Yui Liong
Tropical Marine Science Institute, National University of Singapore,
Singapore
Willis Research Network, Willis Re Inc., London, UK
Center for Environmental Modeling and Sensing, SMART, Singapore
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Saved (final revised paper)
Latest update: 15 Nov 2024
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...