Daily runoff time-series prediction based on the adaptive neural fuzzy inference system

Artificial neural network and fuzzy inference technology have been successfully used in various fields in the last decades. In order to combine the advantages of these approaches, the previous researchers came up with a new model named adaptive neural fuzzy inference system (ANFIS), which has been applied to signal processing and the related fields. Hydrological prediction is an important aspect of hydrological services for economy and society. The prediction result not only provides decision support for generation optimization, but also is of great significance to the economical operation of hydropower systems, navigation, flood control and so on. This paper presents the application of adaptive neural fuzzy inference system (ANFIS) on daily runoff time-series prediction at Tongzilin station. To evaluate the performances of the selected ANFIS, comparison was made with the ANN and autoregressive (AR) model. Previous inflows were chosen as input vectors of the three different models. Nash-Sutcliffe efficiency coefficient (NS coefficient), root mean square error (RMSE) and mean absolute relative error (MARE) were chosen to evaluate the performances of our models. The results show that ANFIS not only keep the potential of the ANN whose advantage is to deal with nonlinear problem, but it also eases the model building process and makes the result more stable. As a result, ANFIS can be a recommended daily runoff time-series prediction model.

[1]  Robert J. Schalkoff,et al.  Artificial neural networks , 1997 .

[2]  Mahmud Güngör,et al.  River flow estimation using adaptive neuro fuzzy inference system , 2007, Math. Comput. Simul..

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

[4]  Taesoon Kim,et al.  Monthly Precipitation Forecasting with a Neuro-Fuzzy Model , 2012, Water Resources Management.

[5]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

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

[7]  K. Lee,et al.  A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer , 2011 .

[8]  Ashish Sharma,et al.  A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting , 2000 .

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

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

[11]  D. K. Srivastava,et al.  Application of ANN for Reservoir Inflow Prediction and Operation , 1999 .

[12]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[13]  C. Young,et al.  Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model , 2015 .

[14]  Ashu Jain,et al.  A comparative analysis of training methods for artificial neural network rainfall-runoff models , 2006, Appl. Soft Comput..

[15]  Deg-Hyo Bae,et al.  Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique , 2007 .

[16]  Vahid Nourani,et al.  An ANN‐based model for spatiotemporal groundwater level forecasting , 2008 .

[17]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[18]  K. P. Sudheer,et al.  Identification of physical processes inherent in artificial neural network rainfall runoff models , 2004 .

[19]  P. C. Nayak,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[20]  Sobri Harun,et al.  Rainfall-Runoff Modeling Using Artificial Neural Network , 2001 .

[21]  Paulin Coulibaly,et al.  Groundwater level forecasting using artificial neural networks , 2005 .

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

[23]  J. Rantala,et al.  Optimised Subtractive Clustering for Neuro-Fuzzy Models , 2022 .

[24]  Michael Y. Hu,et al.  Effect of data standardization on neural network training , 1996 .

[25]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .