Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

[1]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[2]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[3]  R. Valencia,et al.  Disaggregation processes in stochastic hydrology , 1973 .

[4]  R. Shibata.  Selection of the order of an autoregressive model by Akaike's information criterion , 1976 .

[5]  T. Unny,et al.  Extension and application of feature prediction model for synthesis of hydrologic records , 1980 .

[6]  Jose D. Salas,et al.  Physical Basis of Stochastic Models of Annual Flows , 1981 .

[7]  Jery R. Stedinger,et al.  Synthetic streamflow generation: 1. Model verification and validation , 1982 .

[8]  Edward A. McBean,et al.  River Flow Forecasting Model For Sturgeon River , 1985 .

[9]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Keith W. Hipel,et al.  TIME SERIES ANALYSIS IN PERSPECTIVE , 1985 .

[11]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[12]  Isao Hayashi,et al.  NN-driven fuzzy reasoning , 1991, Int. J. Approx. Reason..

[13]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[14]  Jyh-Shing Roger Jang,et al.  Self-learning fuzzy controllers based on temporal backpropagation , 1992, IEEE Trans. Neural Networks.

[15]  Witold F. Krajewski,et al.  Rainfall forecasting in space and time using a neural network , 1992 .

[16]  Yoshiki Uchikawa,et al.  On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm , 1992, IEEE Trans. Neural Networks.

[17]  James M. Keller,et al.  Implementation of conjunctive and disjunctive fuzzy logic rules with neural networks , 1992, Int. J. Approx. Reason..

[18]  Leung Tsang,et al.  A Neural Network Approach to Inversion of Snow Water Equivalent from Passive Microwave Measurements , 1992 .

[19]  James M. Keller,et al.  Evidence aggregation networks for fuzzy logic inference , 1992, IEEE Trans. Neural Networks.

[20]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[21]  Nachimuthu Karunanithi,et al.  Neural Networks for River Flow Prediction , 1994 .

[22]  Z. H. Ashour,et al.  Time Series Models Adoptable for Forecasting Nile Floods and Ethiopian Rainfalls. , 1994 .

[23]  Juan B. Valdés,et al.  Streamflow Forecasting for Han River Basin, Korea , 1994 .

[24]  H. Raman,et al.  Multivariate modelling of water resources time series using artificial neural networks , 1995 .

[25]  Jose D. Salas,et al.  Predicting Streamflows Based on Neural Networks , 1995 .

[26]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[27]  Lucien Duckstein,et al.  Fuzzy Rule-Based Modeling of Reservoir Operation , 1996 .

[28]  Kevin N. Gurney,et al.  An introduction to neural networks , 2018 .

[29]  Timothy K. Gates,et al.  Planning Reservoir Operations with Imprecise Objectives , 1997 .

[30]  K. Thirumalaiah,et al.  River Stage Forecasting Using Artificial Neural Networks , 1998 .

[31]  Ulrich Anders,et al.  Model selection in neural networks , 1999, Neural Networks.

[32]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[33]  Amir F. Atiya,et al.  A comparison between neural-network forecasting techniques-case study: river flow forecasting , 1999, IEEE Trans. Neural Networks.

[34]  A. Tokar,et al.  Rainfall-Runoff Modeling Using Artificial Neural Networks , 1999 .

[35]  Tawatchai Tingsanchali,et al.  Application of tank, NAM, ARMA and neural network models to flood forecasting , 2000 .

[36]  Stan Openshaw,et al.  A hybrid multi-model approach to river level forecasting , 2000 .

[37]  Pao-Shan Yu,et al.  Application of Gray and Fuzzy Methods for Rainfall Forecasting , 2000 .

[38]  Bernard Bobée,et al.  Daily reservoir inflow forecasting using artificial neural networks with stopped training approach , 2000 .

[39]  Asaad Y. Shamseldin,et al.  A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi–Sugeno fuzzy system , 2001 .

[40]  Yen-Chang Chen,et al.  A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction , 2001 .

[41]  B. Bobée,et al.  Improving extreme hydrologic events forecasting using a new criterion for artificial neural network selection , 2001 .

[42]  Li-Chiu Chang,et al.  Intelligent control for modelling of real‐time reservoir operation , 2001 .

[43]  B. Bobée,et al.  Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks , 2001 .

[44]  Rosangela Ballini,et al.  Multi-step-ahead monthly streamflow forecasting by a neurofuzzy network model , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[45]  Beom-Soo Kim,et al.  Fuzzy Logic Model of Langmuir Probe Discharge Data , 2002, Comput. Chem..

[46]  K. P. Sudheer,et al.  A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models , 2002 .

[47]  K. P. Sudheer,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[48]  P. C. Nayak,et al.  Fuzzy computing based rainfall–runoff model for real time flood forecasting , 2005 .

[49]  K. P. Sudheer,et al.  Rainfall‐runoff modelling using artificial neural networks: comparison of network types , 2005 .

[50]  Yong-Huang Lin,et al.  The strategy of building a flood forecast model by neuro‐fuzzy network , 2006 .

[51]  M. Erol Keskin,et al.  Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series , 2006 .

[52]  N. K. Goel,et al.  Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship , 2006 .

[53]  A. K. Lohani,et al.  Reply to comments provided by Z. Şen on “Takagi–Sugeno fuzzy system for modeling stage-discharge relationship” by A.K. Lohani, N.K. Goel and K.K.S. Bhatia , 2007 .

[54]  Ahmed El-Shafie,et al.  A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam , 2007 .

[55]  A. K. Lohani,et al.  Deriving stage–discharge–sediment concentration relationships using fuzzy logic , 2007 .

[56]  Mahmud Güngör,et al.  Hydrological time‐series modelling using an adaptive neuro‐fuzzy inference system , 2008 .

[57]  Dimitri Solomatine,et al.  Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application , 2009 .

[58]  Nelson Obregón-Neira,et al.  Forecasting of Monthly Streamflows Based on Artificial Neural Networks , 2009 .

[59]  Dimitri Solomatine,et al.  Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology , 2009 .

[60]  K. Chau,et al.  Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques , 2009 .

[61]  Ercan Kahya,et al.  Flow forecast by SWAT model and ANN in Pracana basin, Portugal , 2009, Adv. Eng. Softw..

[62]  R. S. Govindaraju,et al.  Artificial Neural Networks in Hydrology , 2010 .

[63]  Martijn J. Booij,et al.  Validation of an ANN Flow Prediction Model Using a Multt-Station Cluster Analysis , 2012 .