Articles | Volume 20, issue 4
Hydrol. Earth Syst. Sci., 20, 1405–1412, 2016
Hydrol. Earth Syst. Sci., 20, 1405–1412, 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 et al.

Related subject area

Subject: Groundwater hydrology | Techniques and Approaches: Stochastic approaches
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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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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.