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

Related subject area

Subject: Groundwater hydrology | Techniques and Approaches: Stochastic approaches
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Influence of low-frequency variability on high and low groundwater levels: example of aquifers in the Paris Basin
Lisa Baulon, Nicolas Massei, Delphine Allier, Matthieu Fournier, and Hélène Bessiere
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Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks
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Technical note: Discharge response of a confined aquifer with variable thickness to temporal, nonstationary, random recharge processes
Ching-Min Chang, Chuen-Fa Ni, We-Ci Li, Chi-Ping Lin, and I-Hsien Lee
Hydrol. Earth Syst. Sci., 25, 2387–2397, https://doi.org/10.5194/hess-25-2387-2021,https://doi.org/10.5194/hess-25-2387-2021, 2021
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Cited articles

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Daliakopoulosa, I. N., Coulibaly, P., and Tsanis, I. K.: Groundwater level forecasting using artificial neural networks, J. Hydrol., 309, 229–240, 2005.
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Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., and Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition, IEEE T. Pattern Anal., 31, 855–868, 2009.
Haykin, S.: Neural Networks: A Comprehensive Foundation, Prentice Hall, New Jersey, 1999.
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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.