A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling

Intelligent computing tools based on fuzzy logic and Artificial Neural Networks (ANN) have been successfully applied in various problems with superior performances. A new approach of combining these two powerful AI tools, known as neuro-fuzzy systems, has increasingly attracted scientists in different fields. Although many studies have been carried out using this approach in pattern recognition and signal processing, few studies have been undertaken to evaluate their performances in hydrologic modeling, specifically rainfall-runoff (R-R) modeling. This study presents an application of an Adaptive Network-based Fuzzy Inference System (ANFIS), as a neuro-fuzzy-computational technique, in event-based R-R modeling in order to evaluate the capabilities of this method for a sub-catchment of Kranji basin in Singapore. Approximately two years of rainfall and runoff data which from 66 separate rainfall events were analyzed in this study. Two different approaches in the selection criteria for calibration events were adopted and the performance of an ANFIS R-R model was compared against an established physically-based model called Storm Water Management Model (SWMM) in R-R modeling. The results of this study show that the selected neuro-fuzzy-computational technique (ANFIS) is comparable to SWMM in event-based R-R modeling. In addition, ANFIS is found to be better at peak flow estimation compared to SWMM. This study demonstrates the promising potential of neuro-fuzzy-computationally inspired hybrid tools in R-R modeling and analysis.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Hiok Chai Quek,et al.  GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms , 2005, Expert Syst. Appl..

[3]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

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

[5]  Hiok Chai Quek,et al.  R-POPTVR: A Novel Reinforcement-Based POPTVR Fuzzy Neural Network for Pattern Classification , 2009, IEEE Transactions on Neural Networks.

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

[7]  Hiok Chai Quek,et al.  A Novel Blood Glucose Regulation Using TSK$^{0}$-FCMAC: A Fuzzy CMAC Based on the Zero-Ordered TSK Fuzzy Inference Scheme , 2009, IEEE Transactions on Neural Networks.

[8]  Hiok Chai Quek,et al.  BIOLOGICAL BRAIN‐INSPIRED GENETIC COMPLEMENTARY LEARNING FOR STOCK MARKET AND BANK FAILURE PREDICTION 1 , 2007, Comput. Intell..

[9]  Michel Pasquier,et al.  POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction , 2006, IEEE Transactions on Intelligent Transportation Systems.

[10]  T. Martin McGinnity,et al.  Predicting a Chaotic Time Series using Fuzzy Neural network , 1998, Inf. Sci..

[11]  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 .

[12]  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 .

[13]  M. Kang,et al.  APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING 1 , 2006 .

[14]  Nikola Kasabov,et al.  Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS): On-line learning and Application for Time-Series Prediction , 2000 .

[15]  Charles S. Melching,et al.  Comparison of Kinematic-Wave and Nonlinear Reservoir Routing of Urban Watershed Runoff , 2005 .

[16]  Hiok Chai Quek,et al.  POP-Yager: A novel self-organizing fuzzy neural network based on the Yager inference , 2005, Expert Syst. Appl..

[17]  H. Halff,et al.  Predicting Runoff from Rainfall Using Neural Networks , 1993 .

[18]  R. E. Kent.  Robert E , 2005 .

[19]  K. P. Sudheer,et al.  Short‐term flood forecasting with a neurofuzzy model , 2005 .

[20]  Hiok Chai Quek,et al.  FITSK: online local learning with generic fuzzy input Takagi-Sugeno-Kang fuzzy framework for nonlinear system estimation , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Kai Keng Ang,et al.  RSPOP: Rough SetBased Pseudo Outer-Product Fuzzy Rule Identification Algorithm , 2005, Neural Computation.

[22]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[23]  Keith Beven,et al.  Robert E. Horton's perceptual model of infiltration processes , 2004 .

[24]  Ruowei Zhou,et al.  A novel single-pass thinning algorithm and an effective set of performance criteria , 1995, Pattern Recognit. Lett..

[25]  Pao-Shan Yu,et al.  Fuzzy multi-objective function for rainfall-runoff model calibration , 2000 .

[26]  Hiok Chai Quek,et al.  HebbR2-Taffic: A novel application of neuro-fuzzy network for visual based traffic monitoring system , 2009, Expert Syst. Appl..

[27]  Edmond Y.M. Lo,et al.  Generation of total runoff hydrographs using a method derived from a digital filter algorithm , 2009 .

[28]  U. C. Kothyari,et al.  Artificial neural networks for daily rainfall—runoff modelling , 2002 .

[29]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[30]  Lloyd H.C. Chua,et al.  Hybrid neural network-finite element river flow model , 2005 .

[31]  Di Wang,et al.  Novel Self-Organizing Takagi Sugeno Kang Fuzzy Neural Networks Based on ART-like Clustering , 2004, Neural Processing Letters.

[32]  Feng Liu,et al.  A Novel Generic Hebbian Ordering-Based Fuzzy Rule Base Reduction Approach to Mamdani Neuro-Fuzzy System , 2007, Neural Computation.

[33]  M Sivakumar,et al.  Comparison of artificial neural network and regression models in the prediction of urban stormwater quality. , 2008, Water environment research : a research publication of the Water Environment Federation.

[34]  Bernard De Baets,et al.  Comparison of data-driven TakagiSugeno models of rainfalldischarge dynamics , 2005 .

[35]  M. Lighthill,et al.  On kinematic waves I. Flood movement in long rivers , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[36]  P. C. Nayak,et al.  Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach , 2006 .

[37]  Hiok Chai Quek,et al.  eFSM—A Novel Online Neural-Fuzzy Semantic Memory Model , 2010, IEEE Transactions on Neural Networks.

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

[39]  A. Bárdossy,et al.  Development of a fuzzy logic-based rainfall-runoff model , 2001 .

[40]  Keith Beven,et al.  Robert Horton’s perceptual model of infiltration. , 2004 .

[41]  Hiok Chai Quek,et al.  A novel approach to the derivation of fuzzy membership functions using the Falcon-MART architecture , 2001, Pattern Recognit. Lett..

[42]  Michael Bruen,et al.  Development of neuro-fuzzy models to account for temporal and spatial variations in a lumped rainfall-runoff model , 2008 .

[43]  J. Yen,et al.  Fuzzy Logic: Intelligence, Control, and Information , 1998 .

[44]  Allen T. Hjelmfelt,et al.  Artificial Neural Networks as Unit Hydrograph Applications , 1993 .

[45]  E. Lo,et al.  Performances of Rainfall-Runoff Models Calibrated over Single and Continuous Storm Flow Events , 2008 .

[46]  Ruowei Zhou,et al.  Antiforgery: a novel pseudo-outer product based fuzzy neural network driven signature verification system , 2002, Pattern Recognit. Lett..

[47]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..

[48]  P. Krause,et al.  COMPARISON OF DIFFERENT EFFICIENCY CRITERIA FOR HYDROLOGICAL MODEL ASSESSMENT , 2005 .