Articles | Volume 14, issue 10
https://doi.org/10.5194/hess-14-1943-2010
© Author(s) 2010. 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-14-1943-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application
A. Elshorbagy
Centre for Advanced Numerical Simulation (CANSIM), Department of Civil and Geological Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada
G. Corzo
Department of Hydroinformatics and Knowledge Management, UNESCO-IHE Institute for Water Education, Delft, The Netherlands
S. Srinivasulu
Centre for Advanced Numerical Simulation (CANSIM), Department of Civil and Geological Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada
D. P. Solomatine
Department of Hydroinformatics and Knowledge Management, UNESCO-IHE Institute for Water Education, Delft, The Netherlands
Water Resources Section, Delft University of Technology, Delft, The Netherlands
Viewed
Total article views: 5,907 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 19 Nov 2009)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
1,982 | 3,784 | 141 | 5,907 | 128 | 128 |
- HTML: 1,982
- PDF: 3,784
- XML: 141
- Total: 5,907
- BibTeX: 128
- EndNote: 128
Total article views: 3,410 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 14 Oct 2010)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
1,708 | 1,581 | 121 | 3,410 | 118 | 122 |
- HTML: 1,708
- PDF: 1,581
- XML: 121
- Total: 3,410
- BibTeX: 118
- EndNote: 122
Total article views: 2,497 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 19 Nov 2009)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
274 | 2,203 | 20 | 2,497 | 10 | 6 |
- HTML: 274
- PDF: 2,203
- XML: 20
- Total: 2,497
- BibTeX: 10
- EndNote: 6
Cited
108 citations as recorded by crossref.
- A support vector machine-based method for improving real-time hourly precipitation forecast in Japan G. Yin et al. 10.1016/j.jhydrol.2022.128125
- Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al. 10.1029/2021WR030993
- Investigating capabilities of machine learning techniques in forecasting stream flow S. Kabir et al. 10.1680/jwama.19.00001
- Sensitivity analysis of data-driven groundwater forecasts to hydroclimatic controls in irrigated croplands A. Amaranto et al. 10.1016/j.jhydrol.2020.124957
- Regionalization of runoff models derived by genetic programming M. Heřmanovský et al. 10.1016/j.jhydrol.2017.02.018
- On the Automation of Flood Event Separation From Continuous Time Series H. Oppel & B. Mewes 10.3389/frwa.2020.00018
- Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data V. Jothiprakash & R. Magar 10.1016/j.jhydrol.2012.04.045
- Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations K. Kasiviswanathan et al. 10.1016/j.jhydrol.2013.06.043
- Prediction of Water Retention of Soils from the Humid Tropics by the Nonparametric k‐Nearest Neighbor Approach Y. Botula et al. 10.2136/vzj2012.0123
- Streamflow estimation using satellite-retrieved water fluxes and machine learning technique over monsoon-dominated catchments of India D. Dayal et al. 10.1080/02626667.2021.1889557
- Developing novel ensemble models for predicting soil hydraulic properties in China’s arid region L. Niu et al. 10.1016/j.jhydrol.2024.131354
- Data-driven modeling for river flood forecasting based on a piecewise linear ARX system identification B. Hadid et al. 10.1016/j.jprocont.2019.12.007
- Product-Units neural networks for catchment runoff forecasting A. Piotrowski & J. Napiorkowski 10.1016/j.advwatres.2012.05.016
- Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds J. Shortridge et al. 10.5194/hess-20-2611-2016
- Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers V. Gholami et al. 10.1016/j.jhydrol.2015.09.028
- Analysis of data characterizing tide and current fluxes in coastal basins E. Armenio et al. 10.5194/hess-21-3441-2017
- Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling S. Galelli & A. Castelletti 10.5194/hess-17-2669-2013
- The Impact of Training Data Sequence on the Performance of Neuro-Fuzzy Rainfall-Runoff Models with Online Learning T. Chang et al. 10.3390/w11010052
- Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming L. Mediero et al. 10.5194/nhess-12-3719-2012
- A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA A. Amaranto et al. 10.1029/2018WR024301
- Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
- Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency in Ensemble Model Simulation H. Foroozand & S. Weijs 10.3390/e19100520
- A flood mitigation control strategy based on the estimation of hydrographs and volume dispatching B. Hadid et al. 10.1016/j.ifacol.2019.11.003
- A hybrid data-driven approach to analyze the drivers of lake level dynamics M. Somogyvári et al. 10.5194/hess-28-4331-2024
- Soil Water Dynamic Modeling Using the Physical and Support Vector Machine Methods K. Lamorski et al. 10.2136/vzj2013.05.0085
- Geospatial modeling using hybrid machine learning approach for flood susceptibility B. Mishra et al. 10.1007/s12145-022-00872-x
- Machine learning based identification of dominant controls on runoff dynamics H. Oppel & A. Schumann 10.1002/hyp.13740
- A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India P. Shekar et al. 10.1016/j.aiig.2024.100073
- A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation E. Tapoglou et al. 10.1016/j.jhydrol.2014.10.040
- 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
- Reconstruction of the pan evaporation based on meteorological factors with machine learning method over China H. Wang et al. 10.1016/j.agwat.2023.108416
- Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. 10.1080/02626667.2016.1159683
- Impact of temperature changes on groundwater levels and irrigation costs in a groundwater-dependent agricultural region in Northwest Bangladesh G. Salem et al. 10.3178/hrl.11.85
- Elucidating the relationship between gaseous O2 and redox potential in a soil aquifer treatment system using data driven approaches and an oxygen diffusion model T. Turkeltaub et al. 10.1016/j.jhydrol.2023.129168
- Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting V. Havlíček et al. 10.1007/s00607-013-0298-0
- A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2012.10.019
- A hybrid model of self organizing maps and least square support vector machine for river flow forecasting S. Ismail et al. 10.5194/hess-16-4417-2012
- Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland A. Amaranto et al. 10.2166/hydro.2018.002
- Pedotransfer functions to predict water retention for soils of the humid tropics: a review Y. Botula et al. 10.1590/S0100-06832014000300001
- Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan M. Tsai et al. 10.1002/hyp.9559
- Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India P. Shekar et al. 10.1007/s10661-023-11649-0
- A Probabilistic Runoff Prediction Model Based on Improved Long Short-Term Memory and Interval Correction S. Zhu et al. 10.1061/JHYEFF.HEENG-6091
- Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
- Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches W. Chang & X. Chen 10.3390/w10091116
- Physically based vs. data-driven models for streamflow and reservoir volume prediction at a data-scarce semi-arid basin G. Özdoğan-Sarıkoç & F. Dadaser-Celik 10.1007/s11356-024-33732-w
- Combined TBATS and SVM model of minimum and maximum air temperatures applied to wheat yield prediction at different locations in Europe M. Gos et al. 10.1016/j.agrformet.2019.107827
- Influence of lag time on event-based rainfall–runoff modeling using the data driven approach A. Talei & L. Chua 10.1016/j.jhydrol.2012.03.027
- Comparing various artificial neural network types for water temperature prediction in rivers A. Piotrowski et al. 10.1016/j.jhydrol.2015.07.044
- Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts J. Zaherpour et al. 10.1088/1748-9326/aac547
- Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s40808-016-0079-9
- Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2011.06.019
- An empirical modeling approach to predict and understand phytoplankton dynamics in a reservoir affected by interbasin water transfers R. Fornarelli et al. 10.1002/wrcr.20268
- Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure T. Chang et al. 10.1016/j.jhydrol.2018.07.074
- Ideal point error for model assessment in data-driven river flow forecasting C. Dawson et al. 10.5194/hess-16-3049-2012
- Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction X. Zhang et al. 10.1007/s11269-020-02514-7
- Evaluation of plotless density estimators in different plant density intensities and distribution patterns H. Jamali et al. 10.1016/j.gecco.2020.e01114
- Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction A. Malik et al. 10.1007/s00477-020-01874-1
- An overview of river flood forecasting procedures in Canadian watersheds Z. Zahmatkesh et al. 10.1080/07011784.2019.1601598
- Moment-based metrics for global sensitivity analysis of hydrological systems A. Dell'Oca et al. 10.5194/hess-21-6219-2017
- Ensemble‐Based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection T. Kim et al. 10.1029/2019WR026262
- Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin Y. Ren et al. 10.3390/w14111692
- Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale G. Tayfur et al. 10.1016/j.jhydrol.2013.12.045
- Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States G. Konapala & A. Mishra 10.1029/2018WR024620
- Reconstructing input for artificial neural networks based on embedding theory and mutual information to simulate soil pore water salinity in tidal floodplain F. Zheng et al. 10.1002/2014WR016875
- Knowledge extraction from trained ANN drought classification model V. Vidyarthi & A. Jain 10.1016/j.jhydrol.2020.124804
- Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture G. Tayfur & L. Brocca 10.1007/s11269-015-1012-0
- Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach J. Ma et al. 10.1155/2020/2624547
- Challenges in Applying Machine Learning Models for Hydrological Inference: A Case Study for Flooding Events Across Germany L. Schmidt et al. 10.1029/2019WR025924
- Comparative analysis of random forest, exploratory regression, and structural equation modeling for screening key environmental variables in evaluating rangeland above-ground biomass N. Kaveh et al. 10.1016/j.ecoinf.2023.102251
- Comparison of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics P. Li et al. 10.1016/j.jhydrol.2020.124692
- Quantification of the predictive uncertainty of artificial neural network based river flow forecast models K. Kasiviswanathan & K. Sudheer 10.1007/s00477-012-0600-2
- River ice breakup timing prediction through stacking multi-type model trees W. Sun 10.1016/j.scitotenv.2018.07.001
- Improving the representation of soil moisture by using a semi‐analytical infiltration model L. Brocca et al. 10.1002/hyp.9766
- Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study J. Kim & J. Ryu 10.3390/w11040823
- Comparison of different methods for reconstruction of instantaneous peak flow data A. Fathzadeh et al. 10.1080/10798587.2015.1120991
- Improving predictions of hydrological low-flow indices in ungaged basins using machine learning S. Worland et al. 10.1016/j.envsoft.2017.12.021
- A comparison of particle swarm optimization and genetic algorithm for daily rainfall-runoff modelling: a case study for Southeast Queensland, Australia M. Jahandideh-Tehrani et al. 10.1007/s11081-020-09538-3
- Supporting M5 model trees with sensitivity information derived from conceptual hydrological models C. Massmann 10.2166/hydro.2015.111
- A linguistic decision tree approach to predicting storm surge S. Royston et al. 10.1016/j.fss.2012.10.001
- Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling W. Wu et al. 10.1016/j.envsoft.2013.12.016
- Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s00477-016-1369-5
- Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches Y. Zhang et al. 10.1029/2018WR023325
- Machine learning models for streamflow regionalization in a tropical watershed R. Ferreira et al. 10.1016/j.jenvman.2020.111713
- Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review V. Nourani et al. 10.1016/j.jhydrol.2014.03.057
- A ranking system for comparing models' performance combining multiple statistical criteria and scenarios: The case of reference evapotranspiration models V. Aschonitis et al. 10.1016/j.envsoft.2019.01.005
- Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS M. Tehrany et al. 10.1016/j.jhydrol.2014.03.008
- Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting A. Ahani et al. 10.1007/s11269-017-1792-5
- Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning A. Talei et al. 10.1016/j.jhydrol.2013.02.022
- Application of rule based methods to predicting storm surge S. Royston et al. 10.1016/j.csr.2012.02.018
- Rainfall-runoff Modeling Using Dynamic Evolving Neural Fuzzy Inference System with Online Learning C. Kwin et al. 10.1016/j.proeng.2016.07.518
- Comparison of Interpolation, Statistical, and Data-Driven Methods for Imputation of Missing Values in a Distributed Soil Moisture Dataset K. Kornelsen & P. Coulibaly 10.1061/(ASCE)HE.1943-5584.0000767
- Improving ANN-based streamflow estimation models for the Upper Indus Basin using satellite-derived snow cover area M. Hassan & I. Hassan 10.1007/s11600-020-00491-4
- Long-range precipitation forecasts using paleoclimate reconstructions in the western United States C. Carrier et al. 10.1007/s11629-014-3360-2
- Frequency based imputation of precipitation F. Dikbas 10.1007/s00477-016-1356-x
- Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach S. Elmahdy et al. 10.3390/rs12172695
- Enhancing accuracy of autoregressive time series forecasting with input selection and wavelet transformation H. Tran et al. 10.2166/hydro.2016.145
- Streamflow simulation methods for ungauged and poorly gauged watersheds A. Loukas & L. Vasiliades 10.5194/nhess-14-1641-2014
- Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations S. Araya & T. Ghezzehei 10.1029/2018WR024357
- A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing M. Rahnamay Naeini et al. 10.3390/w12092373
- Nonlinear regression in environmental sciences using extreme learning machines: A comparative evaluation A. Lima et al. 10.1016/j.envsoft.2015.08.002
- Wave load formulae for prediction of wave-induced forces on a slender pile within pile groups L. Bonakdar et al. 10.1016/j.coastaleng.2015.05.003
- Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
- Determination of Input for Artificial Neural Networks for Flood Forecasting Using the Copula Entropy Method L. Chen et al. 10.1061/(ASCE)HE.1943-5584.0000932
- Comparative Analysis of River Flow Modelling by Using Supervised Learning Technique S. Ismail et al. 10.1088/1742-6596/995/1/012045
- Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques A. Lohani et al. 10.1016/j.jhydrol.2012.03.031
- Time Series Forecasting Using Wavelet-Least Squares Support Vector Machines and Wavelet Regression Models for Monthly Stream Flow Data S. Pandhiani & A. Shabri 10.4236/ojs.2013.33021
- Mountain-river runoff components and their role in the seasonal development of desert-oases in northwest China M. Matin & C. Bourque 10.1016/j.jaridenv.2015.05.011
- River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin M. Akhtar et al. 10.5194/hess-13-1607-2009
103 citations as recorded by crossref.
- A support vector machine-based method for improving real-time hourly precipitation forecast in Japan G. Yin et al. 10.1016/j.jhydrol.2022.128125
- Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al. 10.1029/2021WR030993
- Investigating capabilities of machine learning techniques in forecasting stream flow S. Kabir et al. 10.1680/jwama.19.00001
- Sensitivity analysis of data-driven groundwater forecasts to hydroclimatic controls in irrigated croplands A. Amaranto et al. 10.1016/j.jhydrol.2020.124957
- Regionalization of runoff models derived by genetic programming M. Heřmanovský et al. 10.1016/j.jhydrol.2017.02.018
- On the Automation of Flood Event Separation From Continuous Time Series H. Oppel & B. Mewes 10.3389/frwa.2020.00018
- Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data V. Jothiprakash & R. Magar 10.1016/j.jhydrol.2012.04.045
- Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations K. Kasiviswanathan et al. 10.1016/j.jhydrol.2013.06.043
- Prediction of Water Retention of Soils from the Humid Tropics by the Nonparametric k‐Nearest Neighbor Approach Y. Botula et al. 10.2136/vzj2012.0123
- Streamflow estimation using satellite-retrieved water fluxes and machine learning technique over monsoon-dominated catchments of India D. Dayal et al. 10.1080/02626667.2021.1889557
- Developing novel ensemble models for predicting soil hydraulic properties in China’s arid region L. Niu et al. 10.1016/j.jhydrol.2024.131354
- Data-driven modeling for river flood forecasting based on a piecewise linear ARX system identification B. Hadid et al. 10.1016/j.jprocont.2019.12.007
- Product-Units neural networks for catchment runoff forecasting A. Piotrowski & J. Napiorkowski 10.1016/j.advwatres.2012.05.016
- Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds J. Shortridge et al. 10.5194/hess-20-2611-2016
- Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers V. Gholami et al. 10.1016/j.jhydrol.2015.09.028
- Analysis of data characterizing tide and current fluxes in coastal basins E. Armenio et al. 10.5194/hess-21-3441-2017
- Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling S. Galelli & A. Castelletti 10.5194/hess-17-2669-2013
- The Impact of Training Data Sequence on the Performance of Neuro-Fuzzy Rainfall-Runoff Models with Online Learning T. Chang et al. 10.3390/w11010052
- Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming L. Mediero et al. 10.5194/nhess-12-3719-2012
- A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA A. Amaranto et al. 10.1029/2018WR024301
- Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
- Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency in Ensemble Model Simulation H. Foroozand & S. Weijs 10.3390/e19100520
- A flood mitigation control strategy based on the estimation of hydrographs and volume dispatching B. Hadid et al. 10.1016/j.ifacol.2019.11.003
- A hybrid data-driven approach to analyze the drivers of lake level dynamics M. Somogyvári et al. 10.5194/hess-28-4331-2024
- Soil Water Dynamic Modeling Using the Physical and Support Vector Machine Methods K. Lamorski et al. 10.2136/vzj2013.05.0085
- Geospatial modeling using hybrid machine learning approach for flood susceptibility B. Mishra et al. 10.1007/s12145-022-00872-x
- Machine learning based identification of dominant controls on runoff dynamics H. Oppel & A. Schumann 10.1002/hyp.13740
- A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India P. Shekar et al. 10.1016/j.aiig.2024.100073
- A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation E. Tapoglou et al. 10.1016/j.jhydrol.2014.10.040
- 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
- Reconstruction of the pan evaporation based on meteorological factors with machine learning method over China H. Wang et al. 10.1016/j.agwat.2023.108416
- Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. 10.1080/02626667.2016.1159683
- Impact of temperature changes on groundwater levels and irrigation costs in a groundwater-dependent agricultural region in Northwest Bangladesh G. Salem et al. 10.3178/hrl.11.85
- Elucidating the relationship between gaseous O2 and redox potential in a soil aquifer treatment system using data driven approaches and an oxygen diffusion model T. Turkeltaub et al. 10.1016/j.jhydrol.2023.129168
- Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting V. Havlíček et al. 10.1007/s00607-013-0298-0
- A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2012.10.019
- A hybrid model of self organizing maps and least square support vector machine for river flow forecasting S. Ismail et al. 10.5194/hess-16-4417-2012
- Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland A. Amaranto et al. 10.2166/hydro.2018.002
- Pedotransfer functions to predict water retention for soils of the humid tropics: a review Y. Botula et al. 10.1590/S0100-06832014000300001
- Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan M. Tsai et al. 10.1002/hyp.9559
- Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India P. Shekar et al. 10.1007/s10661-023-11649-0
- A Probabilistic Runoff Prediction Model Based on Improved Long Short-Term Memory and Interval Correction S. Zhu et al. 10.1061/JHYEFF.HEENG-6091
- Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
- Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches W. Chang & X. Chen 10.3390/w10091116
- Physically based vs. data-driven models for streamflow and reservoir volume prediction at a data-scarce semi-arid basin G. Özdoğan-Sarıkoç & F. Dadaser-Celik 10.1007/s11356-024-33732-w
- Combined TBATS and SVM model of minimum and maximum air temperatures applied to wheat yield prediction at different locations in Europe M. Gos et al. 10.1016/j.agrformet.2019.107827
- Influence of lag time on event-based rainfall–runoff modeling using the data driven approach A. Talei & L. Chua 10.1016/j.jhydrol.2012.03.027
- Comparing various artificial neural network types for water temperature prediction in rivers A. Piotrowski et al. 10.1016/j.jhydrol.2015.07.044
- Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts J. Zaherpour et al. 10.1088/1748-9326/aac547
- Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s40808-016-0079-9
- Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2011.06.019
- An empirical modeling approach to predict and understand phytoplankton dynamics in a reservoir affected by interbasin water transfers R. Fornarelli et al. 10.1002/wrcr.20268
- Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure T. Chang et al. 10.1016/j.jhydrol.2018.07.074
- Ideal point error for model assessment in data-driven river flow forecasting C. Dawson et al. 10.5194/hess-16-3049-2012
- Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction X. Zhang et al. 10.1007/s11269-020-02514-7
- Evaluation of plotless density estimators in different plant density intensities and distribution patterns H. Jamali et al. 10.1016/j.gecco.2020.e01114
- Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction A. Malik et al. 10.1007/s00477-020-01874-1
- An overview of river flood forecasting procedures in Canadian watersheds Z. Zahmatkesh et al. 10.1080/07011784.2019.1601598
- Moment-based metrics for global sensitivity analysis of hydrological systems A. Dell'Oca et al. 10.5194/hess-21-6219-2017
- Ensemble‐Based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection T. Kim et al. 10.1029/2019WR026262
- Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin Y. Ren et al. 10.3390/w14111692
- Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale G. Tayfur et al. 10.1016/j.jhydrol.2013.12.045
- Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States G. Konapala & A. Mishra 10.1029/2018WR024620
- Reconstructing input for artificial neural networks based on embedding theory and mutual information to simulate soil pore water salinity in tidal floodplain F. Zheng et al. 10.1002/2014WR016875
- Knowledge extraction from trained ANN drought classification model V. Vidyarthi & A. Jain 10.1016/j.jhydrol.2020.124804
- Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture G. Tayfur & L. Brocca 10.1007/s11269-015-1012-0
- Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach J. Ma et al. 10.1155/2020/2624547
- Challenges in Applying Machine Learning Models for Hydrological Inference: A Case Study for Flooding Events Across Germany L. Schmidt et al. 10.1029/2019WR025924
- Comparative analysis of random forest, exploratory regression, and structural equation modeling for screening key environmental variables in evaluating rangeland above-ground biomass N. Kaveh et al. 10.1016/j.ecoinf.2023.102251
- Comparison of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics P. Li et al. 10.1016/j.jhydrol.2020.124692
- Quantification of the predictive uncertainty of artificial neural network based river flow forecast models K. Kasiviswanathan & K. Sudheer 10.1007/s00477-012-0600-2
- River ice breakup timing prediction through stacking multi-type model trees W. Sun 10.1016/j.scitotenv.2018.07.001
- Improving the representation of soil moisture by using a semi‐analytical infiltration model L. Brocca et al. 10.1002/hyp.9766
- Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study J. Kim & J. Ryu 10.3390/w11040823
- Comparison of different methods for reconstruction of instantaneous peak flow data A. Fathzadeh et al. 10.1080/10798587.2015.1120991
- Improving predictions of hydrological low-flow indices in ungaged basins using machine learning S. Worland et al. 10.1016/j.envsoft.2017.12.021
- A comparison of particle swarm optimization and genetic algorithm for daily rainfall-runoff modelling: a case study for Southeast Queensland, Australia M. Jahandideh-Tehrani et al. 10.1007/s11081-020-09538-3
- Supporting M5 model trees with sensitivity information derived from conceptual hydrological models C. Massmann 10.2166/hydro.2015.111
- A linguistic decision tree approach to predicting storm surge S. Royston et al. 10.1016/j.fss.2012.10.001
- Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling W. Wu et al. 10.1016/j.envsoft.2013.12.016
- Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s00477-016-1369-5
- Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches Y. Zhang et al. 10.1029/2018WR023325
- Machine learning models for streamflow regionalization in a tropical watershed R. Ferreira et al. 10.1016/j.jenvman.2020.111713
- Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review V. Nourani et al. 10.1016/j.jhydrol.2014.03.057
- A ranking system for comparing models' performance combining multiple statistical criteria and scenarios: The case of reference evapotranspiration models V. Aschonitis et al. 10.1016/j.envsoft.2019.01.005
- Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS M. Tehrany et al. 10.1016/j.jhydrol.2014.03.008
- Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting A. Ahani et al. 10.1007/s11269-017-1792-5
- Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning A. Talei et al. 10.1016/j.jhydrol.2013.02.022
- Application of rule based methods to predicting storm surge S. Royston et al. 10.1016/j.csr.2012.02.018
- Rainfall-runoff Modeling Using Dynamic Evolving Neural Fuzzy Inference System with Online Learning C. Kwin et al. 10.1016/j.proeng.2016.07.518
- Comparison of Interpolation, Statistical, and Data-Driven Methods for Imputation of Missing Values in a Distributed Soil Moisture Dataset K. Kornelsen & P. Coulibaly 10.1061/(ASCE)HE.1943-5584.0000767
- Improving ANN-based streamflow estimation models for the Upper Indus Basin using satellite-derived snow cover area M. Hassan & I. Hassan 10.1007/s11600-020-00491-4
- Long-range precipitation forecasts using paleoclimate reconstructions in the western United States C. Carrier et al. 10.1007/s11629-014-3360-2
- Frequency based imputation of precipitation F. Dikbas 10.1007/s00477-016-1356-x
- Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach S. Elmahdy et al. 10.3390/rs12172695
- Enhancing accuracy of autoregressive time series forecasting with input selection and wavelet transformation H. Tran et al. 10.2166/hydro.2016.145
- Streamflow simulation methods for ungauged and poorly gauged watersheds A. Loukas & L. Vasiliades 10.5194/nhess-14-1641-2014
- Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations S. Araya & T. Ghezzehei 10.1029/2018WR024357
- A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing M. Rahnamay Naeini et al. 10.3390/w12092373
- Nonlinear regression in environmental sciences using extreme learning machines: A comparative evaluation A. Lima et al. 10.1016/j.envsoft.2015.08.002
- Wave load formulae for prediction of wave-induced forces on a slender pile within pile groups L. Bonakdar et al. 10.1016/j.coastaleng.2015.05.003
- Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
- Determination of Input for Artificial Neural Networks for Flood Forecasting Using the Copula Entropy Method L. Chen et al. 10.1061/(ASCE)HE.1943-5584.0000932
5 citations as recorded by crossref.
- Comparative Analysis of River Flow Modelling by Using Supervised Learning Technique S. Ismail et al. 10.1088/1742-6596/995/1/012045
- Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques A. Lohani et al. 10.1016/j.jhydrol.2012.03.031
- Time Series Forecasting Using Wavelet-Least Squares Support Vector Machines and Wavelet Regression Models for Monthly Stream Flow Data S. Pandhiani & A. Shabri 10.4236/ojs.2013.33021
- Mountain-river runoff components and their role in the seasonal development of desert-oases in northwest China M. Matin & C. Bourque 10.1016/j.jaridenv.2015.05.011
- River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin M. Akhtar et al. 10.5194/hess-13-1607-2009
Saved (final revised paper)
Latest update: 03 Nov 2024