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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.

Related subject area

Subject: Groundwater hydrology | Techniques and Approaches: Stochastic approaches
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Cited articles

Coulibaly, P., Anctil, F., Aravena, R., and Bobee, B.: Artificial neural network modeling of water table depth fluctuations, Water Resour. Res., 37, 885–896, 2001.
Daliakopoulosa, I. N., Coulibaly, P., and Tsanis, I. K.: Groundwater level forecasting using artificial neural networks, J. Hydrol., 309, 229–240, 2005.
French, M. N., Krajewski, W. F., and Cuykendall, R. R.: Rainfall forecasting in space and time using a neural network, J. Hydrol., 137, 1–31, 1992.
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.
This study applies artificial neural networks (ANN) to predict the groundwater table variations...
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