DOI: 10.5194/hess-20-1405-2016
Scopus记录号: 2-s2.0-84964907059
论文题名: Technical note: Application of artificial neural networks in groundwater table forecasting-a case study in a Singapore swamp forest
作者: Sun Y ; , Wendi D ; , Kim D ; E ; , Liong S ; -Y
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2016
卷: 20, 期: 4 起始页码: 1405
结束页码: 1412
语种: 英语
Scopus关键词: 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]
英文摘要: 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). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78873
Appears in Collections: 气候变化事实与影响
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作者单位: Tropical Marine Science Institute, National University of Singapore, Singapore, Singapore; Willis Research Network, Willis Re Inc., London, United Kingdom; Center for Environmental Modeling and Sensing, SMART, Singapore, Singapore
Recommended Citation:
Sun Y,, Wendi D,, Kim D,et al. Technical note: Application of artificial neural networks in groundwater table forecasting-a case study in a Singapore swamp forest[J]. Hydrology and Earth System Sciences,2016-01-01,20(4)