Technical note: Application of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest

• Published in: Hydrology and earth system sciences 2016-04, Vol.20 (4), p.1405-1412
• Main Authors: Sun, Yabin, Wendi, Dadiyorto, Kim, Dong Eon, Liong, Shie-Yui
• Format: Article
• Language: English
• Subjects: Algorithms; Aquifers; Artificial neural networks; Back propagation; Case studies; Computer applications; Constraint modelling; Data processing; Forecasts and trends; Forest management; Forests; Freshwater; Groundwater; Groundwater levels; Groundwater management; Groundwater resources; Groundwater table; Hydrologic models; Hydrologic regime; Hydrology; Inland water environment; Mathematical models; Neural networks; Neurons; Numerical models; Parameter uncertainty; Physical properties; Rain; Rainfall; Resource management; Swamps; Tables; Water resource management; Water resources; Water table
• ISSN: 1607-7938; 1027-5606; 1607-7938
• DOI: 10.5194/hess-20-1405-2016

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.