Articles | Volume 20, issue 4
https://doi.org/10.5194/hess-20-1405-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-1405-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
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
Tropical Marine Science Institute, National University of Singapore,
Singapore
Dadiyorto Wendi
Tropical Marine Science Institute, National University of Singapore,
Singapore
Dong Eon Kim
Tropical Marine Science Institute, National University of Singapore,
Singapore
Shie-Yui Liong
Tropical Marine Science Institute, National University of Singapore,
Singapore
Willis Research Network, Willis Re Inc., London, UK
Center for Environmental Modeling and Sensing, SMART, Singapore
Viewed
Total article views: 2,820 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Sep 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,682 | 1,036 | 102 | 2,820 | 99 | 97 |
- HTML: 1,682
- PDF: 1,036
- XML: 102
- Total: 2,820
- BibTeX: 99
- EndNote: 97
Total article views: 2,308 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 14 Apr 2016)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,406 | 814 | 88 | 2,308 | 88 | 86 |
- HTML: 1,406
- PDF: 814
- XML: 88
- Total: 2,308
- BibTeX: 88
- EndNote: 86
Total article views: 512 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Sep 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 276 | 222 | 14 | 512 | 11 | 11 |
- HTML: 276
- PDF: 222
- XML: 14
- Total: 512
- BibTeX: 11
- EndNote: 11
Cited
53 citations as recorded by crossref.
- Spatial and temporal downscaling schemes to reconstruct high-resolution GRACE data: A case study in the Tarim River Basin, Northwest China D. Xue et al. 10.1016/j.scitotenv.2023.167908
- 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
- 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
- Machine learning based groundwater prediction in a data-scarce basin of Ghana E. Siabi et al. 10.1080/08839514.2022.2138130
- 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
- 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
- Use of meta-heuristic approach in the estimation of aquifer's response to climate change under shared socioeconomic pathways N. Zeydalinejad & R. Dehghani 10.1016/j.gsd.2022.100882
- 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
- Delineating Variabilities of Groundwater Level Prediction Across the Agriculturally Intensive Transboundary Aquifers of South Asia P. Malakar et al. 10.1021/acsestwater.2c00220
- 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
- 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
- Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches A. Osman et al. 10.1007/s11831-022-09715-w
- Prediction of creep index of soft clays using gene expression programming X. Xue & C. Deng 10.1007/s00500-023-08053-8
- Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms A. Rahman et al. 10.1016/j.advwatres.2020.103595
- A deep neural multi-model ensemble (DNM2E) framework for modelling groundwater levels over Kerala using dynamic variables A. Keerthana & A. Nair 10.1007/s00477-023-02570-6
- Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data O. Kisi et al. 10.1007/s11069-017-2767-9
- Analysis and Prediction of Groundwater Resource Change Under Heavy Precipitation and Ecological Water Replenishment B. Shi et al. 10.2139/ssrn.4106361
- Intelligent approach to predict future groundwater level based on artificial neural networks (ANN) M. Derbela & I. Nouiri 10.1007/s41207-020-00185-9
- Water table prediction through causal reasoning modelling J. Molina & J. García-Aróstegui 10.1016/j.scitotenv.2023.161492
- Improving the resolution of GRACE-based water storage estimates based on machine learning downscaling schemes W. Yin et al. 10.1016/j.jhydrol.2022.128447
- Spatial estimation of water-table depth by artificial neural networks in light of ancillary data M. Pasandi et al. 10.1080/02626667.2017.1349908
- Predicting Groundwater Levels in Ogallala Aquifer Wells Using Hierarchical Cluster Analysis and Artificial Neural Networks L. Oliveira et al. 10.1061/JHYEFF.HEENG-5840
- Groundwater Level Modeling Using Multiobjective Optimization with Hybrid Artificial Intelligence Methods F. Banadkooki & A. Haghighi 10.1007/s10666-023-09938-6
- 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
- Hybrid support vector regression models with algorithm of innovative gunner for the simulation of groundwater level T. Roshni et al. 10.1007/s11600-022-00826-3
- 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
- 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
- Groundwater Level Fluctuations in Coastal Aquifer: Using Artificial Neural Networks to Predict the Impacts of Climatical CMIP6 Scenarios A. Roshani & M. Hamidi 10.1007/s11269-022-03204-2
- A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA A. Amaranto et al. 10.1029/2018WR024301
- B-AMA: A Python-coded protocol to enhance the application of data-driven models in hydrology A. Amaranto & M. Mazzoleni 10.1016/j.envsoft.2022.105609
- Enhancement groundwater level prediction using hybrid ANN-HHO model: case study (Shabestar Plain in Iran) E. Mirzania et al. 10.1007/s12517-023-11584-x
- GCMs simulation-based assessment for the response of the Mediterranean Gaza coastal aquifer to climate-induced changes H. Al-Najjar et al. 10.2166/wcc.2022.339
- Analysis and prediction of the changes in groundwater resources under heavy precipitation and ecological water replenishment B. Shi et al. 10.2166/wcc.2023.348
- 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
- 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 new modelling framework to assess changes in groundwater level I. Kalu et al. 10.1016/j.ejrh.2022.101185
- 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
- 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
- A novel model for prediction of uniaxial compressive strength of rocks X. Xue 10.5802/crmeca.109
- Development and Application of an Integrated Hydrological Model for Singapore Freshwater Swamp Forest Y. Sun et al. 10.1016/j.proeng.2016.07.589
52 citations as recorded by crossref.
- Spatial and temporal downscaling schemes to reconstruct high-resolution GRACE data: A case study in the Tarim River Basin, Northwest China D. Xue et al. 10.1016/j.scitotenv.2023.167908
- 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
- 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
- Machine learning based groundwater prediction in a data-scarce basin of Ghana E. Siabi et al. 10.1080/08839514.2022.2138130
- 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
- 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
- Use of meta-heuristic approach in the estimation of aquifer's response to climate change under shared socioeconomic pathways N. Zeydalinejad & R. Dehghani 10.1016/j.gsd.2022.100882
- 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
- Delineating Variabilities of Groundwater Level Prediction Across the Agriculturally Intensive Transboundary Aquifers of South Asia P. Malakar et al. 10.1021/acsestwater.2c00220
- 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
- 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
- Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches A. Osman et al. 10.1007/s11831-022-09715-w
- Prediction of creep index of soft clays using gene expression programming X. Xue & C. Deng 10.1007/s00500-023-08053-8
- Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms A. Rahman et al. 10.1016/j.advwatres.2020.103595
- A deep neural multi-model ensemble (DNM2E) framework for modelling groundwater levels over Kerala using dynamic variables A. Keerthana & A. Nair 10.1007/s00477-023-02570-6
- Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data O. Kisi et al. 10.1007/s11069-017-2767-9
- Analysis and Prediction of Groundwater Resource Change Under Heavy Precipitation and Ecological Water Replenishment B. Shi et al. 10.2139/ssrn.4106361
- Intelligent approach to predict future groundwater level based on artificial neural networks (ANN) M. Derbela & I. Nouiri 10.1007/s41207-020-00185-9
- Water table prediction through causal reasoning modelling J. Molina & J. García-Aróstegui 10.1016/j.scitotenv.2023.161492
- Improving the resolution of GRACE-based water storage estimates based on machine learning downscaling schemes W. Yin et al. 10.1016/j.jhydrol.2022.128447
- Spatial estimation of water-table depth by artificial neural networks in light of ancillary data M. Pasandi et al. 10.1080/02626667.2017.1349908
- Predicting Groundwater Levels in Ogallala Aquifer Wells Using Hierarchical Cluster Analysis and Artificial Neural Networks L. Oliveira et al. 10.1061/JHYEFF.HEENG-5840
- Groundwater Level Modeling Using Multiobjective Optimization with Hybrid Artificial Intelligence Methods F. Banadkooki & A. Haghighi 10.1007/s10666-023-09938-6
- 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
- Hybrid support vector regression models with algorithm of innovative gunner for the simulation of groundwater level T. Roshni et al. 10.1007/s11600-022-00826-3
- 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
- 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
- Groundwater Level Fluctuations in Coastal Aquifer: Using Artificial Neural Networks to Predict the Impacts of Climatical CMIP6 Scenarios A. Roshani & M. Hamidi 10.1007/s11269-022-03204-2
- A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA A. Amaranto et al. 10.1029/2018WR024301
- B-AMA: A Python-coded protocol to enhance the application of data-driven models in hydrology A. Amaranto & M. Mazzoleni 10.1016/j.envsoft.2022.105609
- Enhancement groundwater level prediction using hybrid ANN-HHO model: case study (Shabestar Plain in Iran) E. Mirzania et al. 10.1007/s12517-023-11584-x
- GCMs simulation-based assessment for the response of the Mediterranean Gaza coastal aquifer to climate-induced changes H. Al-Najjar et al. 10.2166/wcc.2022.339
- Analysis and prediction of the changes in groundwater resources under heavy precipitation and ecological water replenishment B. Shi et al. 10.2166/wcc.2023.348
- 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
- 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 new modelling framework to assess changes in groundwater level I. Kalu et al. 10.1016/j.ejrh.2022.101185
- 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
- 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
- A novel model for prediction of uniaxial compressive strength of rocks X. Xue 10.5802/crmeca.109
1 citations as recorded by crossref.
Saved (final revised paper)
Latest update: 25 Apr 2024
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...
