14 Oct 2010
14 Oct 2010
Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology
A. Elshorbagy et al.
Viewed
Total article views: 4,038 (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 | |
---|---|---|---|---|---|
1,648 | 2,257 | 133 | 4,038 | 116 | 80 |
- HTML: 1,648
- PDF: 2,257
- XML: 133
- Total: 4,038
- BibTeX: 116
- EndNote: 80
Total article views: 2,981 (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 | |
---|---|---|---|---|---|
1,392 | 1,489 | 100 | 2,981 | 102 | 73 |
- HTML: 1,392
- PDF: 1,489
- XML: 100
- Total: 2,981
- BibTeX: 102
- EndNote: 73
Total article views: 1,057 (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 | |
---|---|---|---|---|---|
256 | 768 | 33 | 1,057 | 14 | 7 |
- HTML: 256
- PDF: 768
- XML: 33
- Total: 1,057
- BibTeX: 14
- EndNote: 7
Cited
122 citations as recorded by crossref.
- Enhancing robustness of monthly streamflow forecasting model using gated recurrent unit based on improved grey wolf optimizer X. Zhao et al. 10.1016/j.jhydrol.2021.126607
- Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia A. Garrido-Arévalo et al. 10.3390/w12071973
- Dissolved oxygen prediction using a possibility theory based fuzzy neural network U. Khan & C. Valeo 10.5194/hess-20-2267-2016
- Successive-station monthly streamflow prediction using neuro-wavelet technique A. Danandeh Mehr et al. 10.1007/s12145-013-0141-3
- Investigating capabilities of machine learning techniques in forecasting stream flow S. Kabir et al. 10.1680/jwama.19.00001
- Identifying Regional Models for Flow Duration Curves with Evolutionary Polynomial Regression: Application for Intermittent Streams V. Costa et al. 10.1061/(ASCE)HE.1943-5584.0001873
- River flood prediction using fuzzy neural networks: an investigation on automated network architecture U. Khan et al. 10.2166/wst.2018.107
- 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
- Application of data-driven methods to predict the sodium adsorption rate (SAR) in different climates in Iran E. Rahnama et al. 10.1007/s12517-020-06146-4
- Linear genetic programming application for successive-station monthly streamflow prediction A. Danandeh Mehr et al. 10.1016/j.cageo.2014.04.015
- Linking plant and soil indices for water stress management in black gram A. Khorsand et al. 10.1038/s41598-020-79516-3
- 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
- Meteo and Hydrodynamic Measurements to Detect Physical Processes in Confined Shallow Seas M. Mossa 10.3390/s18010280
- Prediction of Water Retention of Soils from the Humid Tropics by the Nonparametrick-Nearest Neighbor Approach Y. Botula et al. 10.2136/vzj2012.0123
- Hydrological Early Warning System Based on a Deep Learning Runoff Model Coupled with a Meteorological Forecast A. de la Fuente et al. 10.3390/w11091808
- 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
- Product-Units neural networks for catchment runoff forecasting A. Piotrowski & J. Napiorkowski 10.1016/j.advwatres.2012.05.016
- An application of artificial neural networks for modeling formaldehyde emission based on process parameters in particleboard manufacturing process İ. Akyüz et al. 10.1007/s10098-017-1342-0
- 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
- Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction J. Chadalawada et al. 10.1029/2019WR026933
- Evaluating the performance of random forest for large-scale flood discharge simulation L. Schoppa et al. 10.1016/j.jhydrol.2020.125531
- Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers V. Gholami et al. 10.1016/j.jhydrol.2015.09.028
- 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
- Quantile-Based Downscaling of Precipitation Using Genetic Programming: Application to IDF Curves in Saskatoon E. Hassanzadeh et al. 10.1061/(ASCE)HE.1943-5584.0000854
- 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
- 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
- Soil Water Dynamic Modeling Using the Physical and Support Vector Machine Methods K. Lamorski et al. 10.2136/vzj2013.05.0085
- Detecting flood prone areas in Harris County: a GIS based analysis F. Mukherjee & D. Singh 10.1007/s10708-019-09984-2
- Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques T. Chang et al. 10.1016/j.jhydrol.2016.12.024
- Inferring catchment flow path responses using a data-driven model: an exploratory study based on a generalized additive model D. Kundu et al. 10.1080/02626667.2017.1357887
- Predicting standard penetration test N-value from cone penetration test data using artificial neural networks B. Tarawneh 10.1016/j.gsf.2016.02.003
- Machine learning based identification of dominant controls on runoff dynamics H. Oppel & A. Schumann 10.1002/hyp.13740
- Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm H. Ouyang 10.5194/nhess-16-1897-2016
- A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation E. Tapoglou et al. 10.1016/j.jhydrol.2014.10.040
- Evaluation of short-term streamflow prediction methods in Urban river basins X. Huang et al. 10.1016/j.pce.2021.103027
- Skill of large-scale seasonal drought impact forecasts S. Sutanto et al. 10.5194/nhess-20-1595-2020
- Analyzing drought characteristics using copula-based genetic algorithm method H. Kiafar et al. 10.1007/s12517-020-05703-1
- A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks W. Wu et al. 10.1002/2012WR012713
- Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting V. Havlíček et al. 10.1007/s00607-013-0298-0
- How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting V. Moreido et al. 10.3390/w13121696
- 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
- Model structure exploration for index flood regionalization D. Han & W. Jaafar 10.1002/hyp.9436
- 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
- Data-driven techniques for modelling the gross primary production of the páramo vegetation using climate data: Application in the Ecuadorian Andean region V. Minaya et al. 10.1016/j.ecoinf.2016.12.002
- 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
- Pedotransfer functions to predict water retention for soils of the humid tropics: a review Y. Botula et al. 10.1590/S0100-06832014000300001
- Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland A. Amaranto et al. 10.2166/hydro.2018.002
- Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches W. Chang & X. Chen 10.3390/w10091116
- 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
- State-of-the-art review of some artificial intelligence applications in pile foundations M. Shahin 10.1016/j.gsf.2014.10.002
- An evaluation framework for input variable selection algorithms for environmental data-driven models S. Galelli et al. 10.1016/j.envsoft.2014.08.015
- 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
- On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks A. Piotrowski et al. 10.1080/02626667.2015.1085650
- 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
- The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models M. Demirel et al. 10.5194/hess-19-275-2015
- A review of artificial intelligence applications in shallow foundations M. Shahin 10.1179/1939787914Y.0000000058
- Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction X. Zhang et al. 10.1007/s11269-020-02514-7
- Estimation of ANN prediction bounds for the suspended sediment load modeling V. Nourani et al. 10.1088/1755-1315/491/1/012001
- Modular conceptual modelling approach and software for river hydraulic simulations V. Wolfs et al. 10.1016/j.envsoft.2015.05.010
- Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction A. Malik et al. 10.1007/s00477-020-01874-1
- Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling C. Meng et al. 10.3390/w8090407
- A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network E. Ogliari et al. 10.3390/forecast2040022
- Moment-based metrics for global sensitivity analysis of hydrological systems A. Dell'Oca et al. 10.5194/hess-21-6219-2017
- Assessing the effects of climate change on monthly precipitation: Proposing of a downscaling strategy through a case study in Turkey U. Okkan 10.1007/s12205-014-0052-y
- A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
- Artificial neural network model for ozone concentration estimation and Monte Carlo analysis M. Gao et al. 10.1016/j.atmosenv.2018.03.027
- Investigating most appropriate method for estimating suspended sediment load based on error criterias in arid and semi-arid areas (case study of Kardeh Dam watershed stations) H. Mousazadeh et al. 10.1007/s12517-021-08414-3
- Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis U. Khan & C. Valeo 10.3390/w9060381
- Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States G. Konapala & A. Mishra 10.1029/2018WR024620
- Self-Learning Cellular Automata for Forecasting Precipitation from Radar Images H. Li et al. 10.1061/(ASCE)HE.1943-5584.0000646
- 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
- Ideal point error for model assessment in data-driven river flow forecasting C. Dawson et al. 10.5194/hess-16-3049-2012
- Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models B. Mekonnen et al. 10.1080/02626667.2014.935778
- An ensemble simulation approach for artificial neural network: An example from chlorophyll a simulation in Lake Poyang, China J. Huang & J. Gao 10.1016/j.ecoinf.2016.11.012
- 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
- Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals O. Oyebode et al. 10.1016/j.heliyon.2019.e02796
- Regional Modeling of Long-Term and Annual Flow Duration Curves: Reliability for Information Transfer with Evolutionary Polynomial Regression V. Costa & W. Fernandes 10.1061/(ASCE)HE.1943-5584.0002051
- Neural network modeling of hydrological systems: A review of implementation techniques O. Oyebode & D. Stretch 10.1111/nrm.12189
- 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
- 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
- Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study J. Kim & J. Ryu 10.3390/w11040823
- 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
- Supporting M5 model trees with sensitivity information derived from conceptual hydrological models C. Massmann 10.2166/hydro.2015.111
- 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
- Improving predictions of hydrological low-flow indices in ungaged basins using machine learning S. Worland et al. 10.1016/j.envsoft.2017.12.021
- Prediction and Inference of Flow Duration Curves Using Multioutput Neural Networks S. Worland et al. 10.1029/2018WR024463
- 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
- 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
- Machine learning models for streamflow regionalization in a tropical watershed R. Ferreira et al. 10.1016/j.jenvman.2020.111713
- 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
- Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s00477-016-1369-5
- 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
- Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review V. Nourani et al. 10.3390/su13041633
- 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
- Correlation approach in predictor selection for groundwater level forecasting in areas threatened by water deficits J. Kajewska-Szkudlarek et al. 10.2166/hydro.2021.059
- Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques O. Oyebode & D. Ighravwe 10.3390/resources8030156
- Precipitation concentration index management by adaptive neuro-fuzzy methodology D. Petković et al. 10.1007/s10584-017-1907-2
- Frequency based imputation of precipitation F. Dikbas 10.1007/s00477-016-1356-x
- Recent advance in earth observation big data for hydrology L. Chen & L. Wang 10.1080/20964471.2018.1435072
- Approaches for Optimizing the Performance of Adaptive Neuro-Fuzzy Inference System and Least-Squares Support Vector Machine in Precipitation Modeling A. Azad et al. 10.1061/(ASCE)HE.1943-5584.0002069
- Estimation of Moisture Content in Thickened Tailings Dams: Machine Learning Techniques Applied to Remote Sensing Images G. Arancibia et al. 10.1109/ACCESS.2021.3053767
- Assessing the Propagation from Meteorological to Hydrological Drought in the São Francisco River Catchment with Standardized Indexes: Exploratory Analysis, Influential Factors, and Forecasting Strategies V. Costa et al. 10.1061/(ASCE)WR.1943-5452.0001464
- Meteorological Drought Forecasting Based on Climate Signals Using Artificial Neural Network – A Case Study in Khanhhoa Province Vietnam M. Le et al. 10.1016/j.proeng.2016.07.528
- Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture H. Yu et al. 10.1016/j.agwat.2021.107423
- 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
- Bootstrap rank-ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling J. Quilty et al. 10.1002/2015WR016959
- Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
- A nonlinear spatio-temporal lumping of radar rainfall for modeling multi-step-ahead inflow forecasts by data-driven techniques F. Chang & M. Tsai 10.1016/j.jhydrol.2016.01.056
- Predicting the geometry of regime rivers using M5 model tree, multivariate adaptive regression splines and least square support vector regression methods S. Shaghaghi et al. 10.1080/15715124.2018.1546731
- 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
- Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
- 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
120 citations as recorded by crossref.
- Enhancing robustness of monthly streamflow forecasting model using gated recurrent unit based on improved grey wolf optimizer X. Zhao et al. 10.1016/j.jhydrol.2021.126607
- Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia A. Garrido-Arévalo et al. 10.3390/w12071973
- Dissolved oxygen prediction using a possibility theory based fuzzy neural network U. Khan & C. Valeo 10.5194/hess-20-2267-2016
- Successive-station monthly streamflow prediction using neuro-wavelet technique A. Danandeh Mehr et al. 10.1007/s12145-013-0141-3
- Investigating capabilities of machine learning techniques in forecasting stream flow S. Kabir et al. 10.1680/jwama.19.00001
- Identifying Regional Models for Flow Duration Curves with Evolutionary Polynomial Regression: Application for Intermittent Streams V. Costa et al. 10.1061/(ASCE)HE.1943-5584.0001873
- River flood prediction using fuzzy neural networks: an investigation on automated network architecture U. Khan et al. 10.2166/wst.2018.107
- 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
- Application of data-driven methods to predict the sodium adsorption rate (SAR) in different climates in Iran E. Rahnama et al. 10.1007/s12517-020-06146-4
- Linear genetic programming application for successive-station monthly streamflow prediction A. Danandeh Mehr et al. 10.1016/j.cageo.2014.04.015
- Linking plant and soil indices for water stress management in black gram A. Khorsand et al. 10.1038/s41598-020-79516-3
- 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
- Meteo and Hydrodynamic Measurements to Detect Physical Processes in Confined Shallow Seas M. Mossa 10.3390/s18010280
- Prediction of Water Retention of Soils from the Humid Tropics by the Nonparametrick-Nearest Neighbor Approach Y. Botula et al. 10.2136/vzj2012.0123
- Hydrological Early Warning System Based on a Deep Learning Runoff Model Coupled with a Meteorological Forecast A. de la Fuente et al. 10.3390/w11091808
- 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
- Product-Units neural networks for catchment runoff forecasting A. Piotrowski & J. Napiorkowski 10.1016/j.advwatres.2012.05.016
- An application of artificial neural networks for modeling formaldehyde emission based on process parameters in particleboard manufacturing process İ. Akyüz et al. 10.1007/s10098-017-1342-0
- 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
- Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction J. Chadalawada et al. 10.1029/2019WR026933
- Evaluating the performance of random forest for large-scale flood discharge simulation L. Schoppa et al. 10.1016/j.jhydrol.2020.125531
- Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers V. Gholami et al. 10.1016/j.jhydrol.2015.09.028
- 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
- Quantile-Based Downscaling of Precipitation Using Genetic Programming: Application to IDF Curves in Saskatoon E. Hassanzadeh et al. 10.1061/(ASCE)HE.1943-5584.0000854
- 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
- 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
- Soil Water Dynamic Modeling Using the Physical and Support Vector Machine Methods K. Lamorski et al. 10.2136/vzj2013.05.0085
- Detecting flood prone areas in Harris County: a GIS based analysis F. Mukherjee & D. Singh 10.1007/s10708-019-09984-2
- Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques T. Chang et al. 10.1016/j.jhydrol.2016.12.024
- Inferring catchment flow path responses using a data-driven model: an exploratory study based on a generalized additive model D. Kundu et al. 10.1080/02626667.2017.1357887
- Predicting standard penetration test N-value from cone penetration test data using artificial neural networks B. Tarawneh 10.1016/j.gsf.2016.02.003
- Machine learning based identification of dominant controls on runoff dynamics H. Oppel & A. Schumann 10.1002/hyp.13740
- Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm H. Ouyang 10.5194/nhess-16-1897-2016
- A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation E. Tapoglou et al. 10.1016/j.jhydrol.2014.10.040
- Evaluation of short-term streamflow prediction methods in Urban river basins X. Huang et al. 10.1016/j.pce.2021.103027
- Skill of large-scale seasonal drought impact forecasts S. Sutanto et al. 10.5194/nhess-20-1595-2020
- Analyzing drought characteristics using copula-based genetic algorithm method H. Kiafar et al. 10.1007/s12517-020-05703-1
- A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks W. Wu et al. 10.1002/2012WR012713
- Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting V. Havlíček et al. 10.1007/s00607-013-0298-0
- How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting V. Moreido et al. 10.3390/w13121696
- 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
- Model structure exploration for index flood regionalization D. Han & W. Jaafar 10.1002/hyp.9436
- 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
- Data-driven techniques for modelling the gross primary production of the páramo vegetation using climate data: Application in the Ecuadorian Andean region V. Minaya et al. 10.1016/j.ecoinf.2016.12.002
- 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
- Pedotransfer functions to predict water retention for soils of the humid tropics: a review Y. Botula et al. 10.1590/S0100-06832014000300001
- Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland A. Amaranto et al. 10.2166/hydro.2018.002
- Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches W. Chang & X. Chen 10.3390/w10091116
- 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
- State-of-the-art review of some artificial intelligence applications in pile foundations M. Shahin 10.1016/j.gsf.2014.10.002
- An evaluation framework for input variable selection algorithms for environmental data-driven models S. Galelli et al. 10.1016/j.envsoft.2014.08.015
- 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
- On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks A. Piotrowski et al. 10.1080/02626667.2015.1085650
- 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
- The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models M. Demirel et al. 10.5194/hess-19-275-2015
- A review of artificial intelligence applications in shallow foundations M. Shahin 10.1179/1939787914Y.0000000058
- Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction X. Zhang et al. 10.1007/s11269-020-02514-7
- Estimation of ANN prediction bounds for the suspended sediment load modeling V. Nourani et al. 10.1088/1755-1315/491/1/012001
- Modular conceptual modelling approach and software for river hydraulic simulations V. Wolfs et al. 10.1016/j.envsoft.2015.05.010
- Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction A. Malik et al. 10.1007/s00477-020-01874-1
- Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling C. Meng et al. 10.3390/w8090407
- A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network E. Ogliari et al. 10.3390/forecast2040022
- Moment-based metrics for global sensitivity analysis of hydrological systems A. Dell'Oca et al. 10.5194/hess-21-6219-2017
- Assessing the effects of climate change on monthly precipitation: Proposing of a downscaling strategy through a case study in Turkey U. Okkan 10.1007/s12205-014-0052-y
- A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
- Artificial neural network model for ozone concentration estimation and Monte Carlo analysis M. Gao et al. 10.1016/j.atmosenv.2018.03.027
- Investigating most appropriate method for estimating suspended sediment load based on error criterias in arid and semi-arid areas (case study of Kardeh Dam watershed stations) H. Mousazadeh et al. 10.1007/s12517-021-08414-3
- Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis U. Khan & C. Valeo 10.3390/w9060381
- Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States G. Konapala & A. Mishra 10.1029/2018WR024620
- Self-Learning Cellular Automata for Forecasting Precipitation from Radar Images H. Li et al. 10.1061/(ASCE)HE.1943-5584.0000646
- 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
- Ideal point error for model assessment in data-driven river flow forecasting C. Dawson et al. 10.5194/hess-16-3049-2012
- Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models B. Mekonnen et al. 10.1080/02626667.2014.935778
- An ensemble simulation approach for artificial neural network: An example from chlorophyll a simulation in Lake Poyang, China J. Huang & J. Gao 10.1016/j.ecoinf.2016.11.012
- 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
- Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals O. Oyebode et al. 10.1016/j.heliyon.2019.e02796
- Regional Modeling of Long-Term and Annual Flow Duration Curves: Reliability for Information Transfer with Evolutionary Polynomial Regression V. Costa & W. Fernandes 10.1061/(ASCE)HE.1943-5584.0002051
- Neural network modeling of hydrological systems: A review of implementation techniques O. Oyebode & D. Stretch 10.1111/nrm.12189
- 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
- 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
- Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study J. Kim & J. Ryu 10.3390/w11040823
- 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
- Supporting M5 model trees with sensitivity information derived from conceptual hydrological models C. Massmann 10.2166/hydro.2015.111
- 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
- Improving predictions of hydrological low-flow indices in ungaged basins using machine learning S. Worland et al. 10.1016/j.envsoft.2017.12.021
- Prediction and Inference of Flow Duration Curves Using Multioutput Neural Networks S. Worland et al. 10.1029/2018WR024463
- 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
- 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
- Machine learning models for streamflow regionalization in a tropical watershed R. Ferreira et al. 10.1016/j.jenvman.2020.111713
- 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
- Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s00477-016-1369-5
- 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
- Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review V. Nourani et al. 10.3390/su13041633
- 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
- Correlation approach in predictor selection for groundwater level forecasting in areas threatened by water deficits J. Kajewska-Szkudlarek et al. 10.2166/hydro.2021.059
- Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques O. Oyebode & D. Ighravwe 10.3390/resources8030156
- Precipitation concentration index management by adaptive neuro-fuzzy methodology D. Petković et al. 10.1007/s10584-017-1907-2
- Frequency based imputation of precipitation F. Dikbas 10.1007/s00477-016-1356-x
- Recent advance in earth observation big data for hydrology L. Chen & L. Wang 10.1080/20964471.2018.1435072
- Approaches for Optimizing the Performance of Adaptive Neuro-Fuzzy Inference System and Least-Squares Support Vector Machine in Precipitation Modeling A. Azad et al. 10.1061/(ASCE)HE.1943-5584.0002069
- Estimation of Moisture Content in Thickened Tailings Dams: Machine Learning Techniques Applied to Remote Sensing Images G. Arancibia et al. 10.1109/ACCESS.2021.3053767
- Assessing the Propagation from Meteorological to Hydrological Drought in the São Francisco River Catchment with Standardized Indexes: Exploratory Analysis, Influential Factors, and Forecasting Strategies V. Costa et al. 10.1061/(ASCE)WR.1943-5452.0001464
- Meteorological Drought Forecasting Based on Climate Signals Using Artificial Neural Network – A Case Study in Khanhhoa Province Vietnam M. Le et al. 10.1016/j.proeng.2016.07.528
- Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture H. Yu et al. 10.1016/j.agwat.2021.107423
- 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
- Bootstrap rank-ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling J. Quilty et al. 10.1002/2015WR016959
- Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
- A nonlinear spatio-temporal lumping of radar rainfall for modeling multi-step-ahead inflow forecasts by data-driven techniques F. Chang & M. Tsai 10.1016/j.jhydrol.2016.01.056
- Predicting the geometry of regime rivers using M5 model tree, multivariate adaptive regression splines and least square support vector regression methods S. Shaghaghi et al. 10.1080/15715124.2018.1546731
- 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
- Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
2 citations as recorded by crossref.
- 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
Saved (final revised paper)
Saved (preprint)
Latest update: 18 May 2022