Neural network modeling of hydrological systems: A review of implementation techniques
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[1] A. W. Minns,et al. Artificial neural networks as rainfall-runoff models , 1996 .
[2] Josiah Adeyemo,et al. Reservoir Inflow Forecasting Using Differential Evolution Trained Neural Networks , 2014 .
[3] R. Deo,et al. Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm , 2017 .
[4] Chuntian Cheng,et al. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series , 2009 .
[5] J. G. Ndiritu,et al. Application of radial basis function neural networks to short-term streamflow forecasting , 2010 .
[6] M. H. Esfe. Designing a neural network for predicting the heat transfer and pressure drop characteristics of Ag/water nanofluids in a heat exchanger , 2017 .
[7] David Lehký,et al. Neural network ensemble-based parameter sensitivity analysis in civil engineering systems , 2017, Neural Computing and Applications.
[8] E. Toth. Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting , 2009 .
[9] Arash Adib,et al. Prediction of suspended sediment load using ANN GA conjunction model with Markov chain approach at flood conditions , 2017 .
[10] Dimitri Solomatine,et al. A novel approach to parameter uncertainty analysis of hydrological models using neural networks , 2009 .
[11] T. Hu,et al. Rainfall–runoff modeling using principal component analysis and neural network , 2007 .
[12] A. M. Kalteh,et al. Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application , 2008, Environ. Model. Softw..
[13] Ali Osman Pektas,et al. Long-range forecasting of suspended sediment , 2017 .
[14] Paulin Coulibaly,et al. Temporal neural networks for downscaling climate variability and extremes , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[15] Soichi Nishiyama,et al. A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff , 2007 .
[16] Avi Ostfeld,et al. Data-driven modelling: some past experiences and new approaches , 2008 .
[17] Ashu Jain,et al. Hybrid neural network models for hydrologic time series forecasting , 2007, Appl. Soft Comput..
[18] N. Null. Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .
[19] P. Gelder,et al. Forecasting daily streamflow using hybrid ANN models , 2006 .
[20] Robert J. Abrahart,et al. Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments , 2002 .
[21] Wei Sun,et al. Multiple model combination methods for annual maximum water level prediction during river ice breakup , 2018 .
[22] O. Kisi. Neural Networks and Wavelet Conjunction Model for Intermittent Streamflow Forecasting , 2009 .
[23] Holger R. Maier,et al. Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..
[24] Ali Osman Pektas,et al. Investigating the extrapolation performance of neural network models in suspended sediment data , 2017 .
[25] Paulin Coulibaly,et al. Bayesian neural network for rainfall‐runoff modeling , 2006 .
[26] Orazio Giustolisi,et al. Optimal design of artificial neural networks by a multi-objective strategy: groundwater level predictions , 2006 .
[27] Josiah Adeyemo,et al. River Flow Forecasting Using an Improved Artificial Neural Network , 2015, EVOLVE.
[28] C. L. Wu,et al. Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis , 2011 .
[29] L. Wang,et al. A DE-based approach to no-wait flow-shop scheduling , 2009, Comput. Ind. Eng..
[30] Roberto Baratti,et al. River flow forecast for reservoir management through neural networks , 2003, Neurocomputing.
[31] R. Arunkumar,et al. Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation , 2017, Journal of The Institution of Engineers (India): Series A.
[32] Paulin Coulibaly,et al. Comparison of neural network methods for infilling missing daily weather records , 2007 .
[33] Dimitri P. Solomatine,et al. Neural networks and M5 model trees in modelling water level-discharge relationship , 2005, Neurocomputing.
[34] Shrikant Charhate,et al. Applications of soft tools to solve hydrological problems for an integrated Indian catchment , 2017 .
[35] Faming Liang,et al. Estimating uncertainty of streamflow simulation using Bayesian neural networks , 2009 .
[36] Lluís Corominas,et al. Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques , 2017, Environ. Model. Softw..
[37] V. R. Shinde,et al. Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok , 2011 .
[38] Vahid Nourani,et al. Emotional ANN (EANN) and Wavelet-ANN (WANN) Approaches for Markovian and Seasonal Based Modeling of Rainfall-Runoff Process , 2018, Water Resources Management.
[39] Dimitri Solomatine,et al. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology , 2009 .
[40] Bidyadhar Subudhi,et al. Nonlinear system identification using memetic differential evolution trained neural networks , 2011, Neurocomputing.
[41] Holger R. Maier,et al. Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach , 2009 .
[42] Jim Duggan,et al. A multi-objective neural network trained with differential evolution for dynamic economic emission dispatch , 2018, International Journal of Electrical Power & Energy Systems.
[43] P. Coulibaly,et al. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting , 2012 .
[44] Holger R. Maier,et al. Input determination for neural network models in water resources applications. Part 1—background and methodology , 2005 .
[45] K. P. Sudheer,et al. Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions , 2010, Environ. Model. Softw..
[46] K. P. Sudheer,et al. Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models , 2017, Stochastic Environmental Research and Risk Assessment.
[47] Yen-Chang Chen,et al. A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction , 2001 .
[48] François Anctil,et al. Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Streamflow Forecasting , 2010 .
[49] Christian W. Dawson,et al. Hydrological modelling using artificial neural networks , 2001 .
[50] Shreenivas Londhe,et al. A novel approach for knowledge extraction from Artificial Neural Networks , 2019 .
[51] Shahab Araghinejad,et al. A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions , 2018, Water Resources Management.
[52] O. Kisi,et al. A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System , 2017 .
[53] Bellie Sivakumar,et al. River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches , 2002 .
[54] Rodney A. Stewart,et al. ANN-based residential water end-use demand forecasting model , 2013, Expert Syst. Appl..
[55] Hui Li,et al. Evolutionary artificial neural networks: a review , 2011, Artificial Intelligence Review.
[56] Alex J. Cannon,et al. Daily streamflow forecasting by machine learning methods with weather and climate inputs , 2012 .
[57] John A. Dracup,et al. Artificial Neural Networks and Long-Range Precipitation Prediction in California , 2000 .
[58] I-Fan Chang,et al. Support vector regression for real-time flood stage forecasting , 2006 .
[59] Ozgur Kisi,et al. River suspended sediment concentration modeling using a neural differential evolution approach , 2010 .
[60] Shreenivas Londhe,et al. Comparison of data-driven modelling techniques for river flow forecasting , 2010 .