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 Sun1, Dadiyorto Wendi1, Dong Eon Kim1, and Shie-Yui Liong1,2,3 Yabin Sun et al.
  • 1Tropical Marine Science Institute, National University of Singapore, Singapore
  • 2Willis Research Network, Willis Re Inc., London, UK
  • 3Center for Environmental Modeling and Sensing, SMART, Singapore

Abstract. Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost, and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a freshwater swamp forest of Singapore. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce an accurate forecast with a leading time of 1 day, whereas the performance decreases when leading time increases to 3 and 7 days.

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.