Please use this identifier to cite or link to this item: https://doi.org/10.5194/hess-20-1405-2016
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
Groundwater resources
Neural networks
Reservoirs (water)
Wetlands
Accurate prediction
Computational costs
Efficient managements
Ground water table
Hydrological regime
Parameter uncertainty
Physical parameters
Physical systems
Forecasting
artificial neural network
forecasting method
groundwater
groundwater resource
hydrological regime
numerical model
performance assessment
reservoir
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. https://doi.org/10.5194/hess-20-1405-2016
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
URI: https://scholarbank.nus.edu.sg/handle/10635/176131
ISSN: 1027-5606
DOI: 10.5194/hess-20-1405-2016
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_5194_hess-20-1405-2016.pdf1.7 MBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

22
checked on Mar 3, 2021

Page view(s)

35
checked on Mar 5, 2021

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.