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Title: Technical note: Application of artificial neural networks in groundwater table forecasting-a case study in a Singapore swamp forest
Authors: Sun, Y 
Wendi, D 
Kim, D.E 
Liong, S.-Y 
Keywords: Forestry
Groundwater resources
Neural networks
Reservoirs (water)
Accurate prediction
Computational costs
Efficient managements
Ground water table
Hydrological regime
Parameter uncertainty
Physical parameters
Physical systems
artificial neural network
forecasting method
groundwater resource
hydrological regime
numerical model
performance assessment
swamp forest
water table
Singapore [Southeast Asia]
Issue Date: 2016
Citation: Sun, Y, Wendi, D, Kim, D.E, Liong, S.-Y (2016). Technical note: Application of artificial neural networks in groundwater table forecasting-a case study in a Singapore swamp forest. Hydrology and Earth System Sciences 20 (4) : 1405-1412. ScholarBank@NUS Repository.
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. © 2016 Author(s).
Source Title: Hydrology and Earth System Sciences
ISSN: 1027-5606
DOI: 10.5194/hess-20-1405-2016
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