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 Sun et al.
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Cited
34 citations as recorded by crossref.
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- High Resolution Water Table Modeling of the Shallow Groundwater Using a Knowledge-Guided Gradient Boosting Decision Tree Model J. Koch et al. 10.3389/frwa.2021.701726
- Spatial estimation of water-table depth by artificial neural networks in light of ancillary data M. Pasandi et al. 10.1080/02626667.2017.1349908
- Support vector machine and data assimilation framework for Groundwater Level Forecasting using GRACE satellite data D. Liu et al. 10.1016/j.jhydrol.2021.126929
- A novel hybrid of support vector regression and metaheuristic algorithms for groundwater spring potential mapping S. Paryani et al. 10.1016/j.scitotenv.2021.151055
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- Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe Y. Ma et al. 10.5194/hess-25-3555-2021
- Application of artificial intelligence in groundwater ecosystem protection: a case study of Semnan/Sorkheh plain, Iran A. Khoshand 10.1007/s10668-021-01361-9
- Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques S. Samantaray & A. Sahoo 10.3233/KES-210066
- Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland A. Amaranto et al. 10.2166/hydro.2018.002
- Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization K. Khosravi et al. 10.5194/hess-22-4771-2018
- A proposed modelling towards the potential impacts of climate change on a semi-arid, small-scaled aquifer: a case study of Iran H. Nassery et al. 10.1007/s10661-021-08955-w
- Exploring geometrical patterns in streamflow time series: utility for forecasting? R. Teegavarapu 10.2166/nh.2018.127
- Groundwater Level Prediction/Forecasting and Assessment of Uncertainty Using SGS and ARIMA Models: A Case Study in the Bauru Aquifer System (Brazil) E. Takafuji et al. 10.1007/s11053-018-9403-6
- Comparison of physical and data-driven models to forecast groundwater level changes with the inclusion of GRACE – A case study over the state of Victoria, Australia W. Yin et al. 10.1016/j.jhydrol.2021.126735
- NGS barcoding reveals high resistance of a hyperdiverse chironomid (Diptera) swamp fauna against invasion from adjacent freshwater reservoirs B. Baloğlu et al. 10.1186/s12983-018-0276-7
- A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA A. Amaranto et al. 10.1029/2018WR024301
- Prediction of the karstic spring flow rates under climate change by climatic variables based on the artificial neural network: a case study of Iran N. Zeydalinejad et al. 10.1007/s10661-020-08332-z
- Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China C. Ao et al. 10.1016/j.agwat.2021.107032
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- Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches A. Osman et al. 10.1007/s11831-022-09715-w
- Impact of a new geological modelling method on the enhancement of the CO2 storage assessment of E sequence of Nam Vang field, offshore Vietnam H. Vo Thanh et al. 10.1080/15567036.2019.1604865
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- Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India J. Mohapatra et al. 10.1016/j.scitotenv.2021.147319
- Groundwater Recharge Prediction Using Linear Regression, Multi-Layer Perception Network, and Deep Learning X. Huang et al. 10.3390/w11091879
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- Development and Application of an Integrated Hydrological Model for Singapore Freshwater Swamp Forest Y. Sun et al. 10.1016/j.proeng.2016.07.589
33 citations as recorded by crossref.
- Intelligent approach to predict future groundwater level based on artificial neural networks (ANN) M. Derbela & I. Nouiri 10.1007/s41207-020-00185-9
- High Resolution Water Table Modeling of the Shallow Groundwater Using a Knowledge-Guided Gradient Boosting Decision Tree Model J. Koch et al. 10.3389/frwa.2021.701726
- Spatial estimation of water-table depth by artificial neural networks in light of ancillary data M. Pasandi et al. 10.1080/02626667.2017.1349908
- Support vector machine and data assimilation framework for Groundwater Level Forecasting using GRACE satellite data D. Liu et al. 10.1016/j.jhydrol.2021.126929
- A novel hybrid of support vector regression and metaheuristic algorithms for groundwater spring potential mapping S. Paryani et al. 10.1016/j.scitotenv.2021.151055
- Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling O. Adeyemi et al. 10.3390/s18103408
- A generic data-driven technique for forecasting of reservoir inflow: Application for hydropower maximization S. Ahmad & F. Hossain 10.1016/j.envsoft.2019.06.008
- Predicting areas with ecotourism capability using artificial neural networks and linear discriminant analysis (case study: Arasbaran Protected Area, Iran) M. Talebi et al. 10.1007/s10668-020-00964-y
- Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe Y. Ma et al. 10.5194/hess-25-3555-2021
- Application of artificial intelligence in groundwater ecosystem protection: a case study of Semnan/Sorkheh plain, Iran A. Khoshand 10.1007/s10668-021-01361-9
- Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques S. Samantaray & A. Sahoo 10.3233/KES-210066
- Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland A. Amaranto et al. 10.2166/hydro.2018.002
- Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization K. Khosravi et al. 10.5194/hess-22-4771-2018
- A proposed modelling towards the potential impacts of climate change on a semi-arid, small-scaled aquifer: a case study of Iran H. Nassery et al. 10.1007/s10661-021-08955-w
- Exploring geometrical patterns in streamflow time series: utility for forecasting? R. Teegavarapu 10.2166/nh.2018.127
- Groundwater Level Prediction/Forecasting and Assessment of Uncertainty Using SGS and ARIMA Models: A Case Study in the Bauru Aquifer System (Brazil) E. Takafuji et al. 10.1007/s11053-018-9403-6
- Comparison of physical and data-driven models to forecast groundwater level changes with the inclusion of GRACE – A case study over the state of Victoria, Australia W. Yin et al. 10.1016/j.jhydrol.2021.126735
- NGS barcoding reveals high resistance of a hyperdiverse chironomid (Diptera) swamp fauna against invasion from adjacent freshwater reservoirs B. Baloğlu et al. 10.1186/s12983-018-0276-7
- A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA A. Amaranto et al. 10.1029/2018WR024301
- Prediction of the karstic spring flow rates under climate change by climatic variables based on the artificial neural network: a case study of Iran N. Zeydalinejad et al. 10.1007/s10661-020-08332-z
- Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China C. Ao et al. 10.1016/j.agwat.2021.107032
- Ensemble modelling framework for groundwater level prediction in urban areas of India B. Yadav et al. 10.1016/j.scitotenv.2019.135539
- Long-Term Groundwater Level Prediction Model Based on Hybrid KNN-RF Technique O. Kombo et al. 10.3390/hydrology7030059
- Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models R. Barzegar et al. 10.1016/j.scitotenv.2017.04.189
- Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches A. Osman et al. 10.1007/s11831-022-09715-w
- Impact of a new geological modelling method on the enhancement of the CO2 storage assessment of E sequence of Nam Vang field, offshore Vietnam H. Vo Thanh et al. 10.1080/15567036.2019.1604865
- A review of the artificial intelligence methods in groundwater level modeling T. Rajaee et al. 10.1016/j.jhydrol.2018.12.037
- Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India J. Mohapatra et al. 10.1016/j.scitotenv.2021.147319
- Groundwater Recharge Prediction Using Linear Regression, Multi-Layer Perception Network, and Deep Learning X. Huang et al. 10.3390/w11091879
- Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms A. Rahman et al. 10.1016/j.advwatres.2020.103595
- Formulating a strategy to combine artificial intelligence models using Bayesian model averaging to study a distressed aquifer with sparse data availability M. Moazamnia et al. 10.1016/j.jhydrol.2019.02.011
- Improving the spatial resolution of GRACE-based groundwater storage estimates using a machine learning algorithm and hydrological model W. Yin et al. 10.1007/s10040-021-02447-4
- Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data O. Kisi et al. 10.1007/s11069-017-2767-9
1 citations as recorded by crossref.
Saved (final revised paper)
Latest update: 27 Jan 2023
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.
This study applies artificial neural networks (ANN) to predict the groundwater table variations...