A new hybrid artificial neural networks for rainfall-runoff process modeling

This paper proposes a hybrid intelligent model for runoff prediction. The proposed model is a combination of data preprocessing methods, genetic algorithms and levenberg-marquardt (LM) algorithm for learning feed forward neural networks. Actually it evolves neural network initial weights for tuning with LM algorithm by using genetic algorithm. We also use data pre-processing methods such as data transformation, input variables selection and data clustering for improving the accuracy of the model. The capability of the proposed method is tested by applying it to predict runoff at the Aghchai watershed. The results show that this approach is able to predict runoff more accurately than Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models.

[1]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[2]  Driss Ouazar,et al.  Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting , 2009, Expert Syst. Appl..

[3]  Ozgur Kisi,et al.  Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process , 2011 .

[4]  Seyed Reza Hejazi,et al.  A new hybrid for improvement of auto-regressive integrated moving average models applying particle swarm optimization , 2012, Expert Syst. Appl..

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

[6]  Ronny Berndtsson,et al.  Monthly runoff simulation: Comparing and combining conceptual and neural network models , 2006 .

[7]  Ali Azadeh,et al.  An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments , 2010 .

[8]  James B. McDonald,et al.  Time Series Prediction With Genetic‐Algorithm Designed Neural Networks: An Empirical Comparison With Modern Statistical Models , 1999, Comput. Intell..

[9]  Xiaohui Lei,et al.  Flood simulation using parallel genetic algorithm integrated wavelet neural networks , 2011, Neurocomputing.

[10]  Pei-Chann Chang,et al.  Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry , 2009, Knowl. Based Syst..

[11]  Jatinder N. D. Gupta,et al.  Comparative evaluation of genetic algorithm and backpropagation for training neural networks , 2000, Inf. Sci..

[12]  M. Esmel ElAlami A filter model for feature subset selection based on genetic algorithm , 2009, Knowl. Based Syst..

[13]  J. A. Chen,et al.  A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm , 2004, Expert Syst. Appl..

[14]  Paul Leahy,et al.  Structural optimisation and input selection of an artificial neural network for river level prediction , 2008 .

[15]  S. Silva-Martínez,et al.  Optimal performance of COD removal during aqueous treatment of alazine and gesaprim commercial herbicides by direct and inverse neural network , 2011 .

[16]  Randall S Sexton,et al.  Employee turnover: a neural network solution , 2005, Comput. Oper. Res..

[17]  Chuntian Cheng,et al.  Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration , 2002 .

[18]  Peter J. Fleming,et al.  A new formulation of the learning problem of a neural network controller , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[19]  Robert E. Dorsey,et al.  Genetic algorithms for estimation problems with multiple optima , 1995 .

[20]  J. Andrew Ware,et al.  Residential property price time series forecasting with neural networks , 2002, Knowl. Based Syst..

[21]  Marco Franchini,et al.  Case Study: Improving Real-Time Stage Forecasting Muskingum Model by Incorporating the Rating Curve Model , 2011 .

[22]  David Corne,et al.  Evolving Neural Networks for Cancer Radiotherapy , 2000 .

[23]  Randall S. Sexton,et al.  Improving Decision Effectiveness of Artificial Neural Networks: A Modified Genetic Algorithm Approach , 2003, Decis. Sci..

[24]  Robert J. Abrahart,et al.  Symbiotic adaptive neuro-evolution applied to rainfall-runoff modelling in northern England , 2006, Neural Networks.

[25]  Parham Soltani,et al.  Acoustic performance of woven fabrics in relation to structural parameters and air permeability , 2013 .

[26]  M. R. Brown,et al.  Neural network and GA approaches for dwelling fire occurrence prediction , 2006, Knowl. Based Syst..

[27]  Mohammad Hossein Fazel Zarandi,et al.  A hybrid fuzzy intelligent agent‐based system for stock price prediction , 2012, Int. J. Intell. Syst..

[28]  Arash Ghanbari,et al.  Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals , 2012, Appl. Soft Comput..

[29]  S. Simonovic,et al.  An Artificial Neural Network model for generating hydrograph from hydro-meteorological parameters , 2005 .

[30]  Mohebbat Mohebbi,et al.  An empowered adaptive neuro-fuzzy inference system using self-organizing map clustering to predict mass transfer kinetics in deep-fat frying of ostrich meat plates , 2011 .

[31]  金熙照 A study on Siro , 2007 .

[32]  P. C. Nayak,et al.  Improving peak flow estimates in artificial neural network river flow models , 2003 .

[33]  Ya Xiong Zhang,et al.  Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis. , 2007, Talanta.

[34]  Y. R. Satyaji Rao,et al.  Time Series Modeling of River Flow Using Wavelet Neural Networks , 2011 .

[35]  James F. Frenzel,et al.  Training product unit neural networks with genetic algorithms , 1993, IEEE Expert.

[36]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[37]  Esmaeil Hadavandi,et al.  A study on siro, solo, compact, and conventional ring-spun yarns. Part III: modeling fiber migration using modular adaptive neuro-fuzzy inference system , 2013 .

[38]  Aytürk Keles,et al.  The adaptive neuro-fuzzy model for forecasting the domestic debt , 2008, Knowl. Based Syst..

[39]  Ben S. Gerber,et al.  Use of genetic algorithms for neural networks to predict community-acquired pneumonia , 2004, Artif. Intell. Medicine.

[40]  Esmaeil Hadavandi,et al.  Developing a hybrid intelligent model for forecasting problems: Case study of tourism demand time series , 2013, Knowl. Based Syst..

[41]  K. P. Sudheer,et al.  Rainfall‐runoff modeling through hybrid intelligent system , 2007 .

[42]  Pei-Chann Chang,et al.  Evolving neural network for printed circuit board sales forecasting , 2005, Expert Syst. Appl..

[43]  R L Lieber,et al.  Stepwise regression is an alternative to splines for fitting noisy data. , 1996, Journal of biomechanics.

[44]  E. Fernández,et al.  Finding Optimal Neural Network Architecture Using Genetic Algorithms , 2007 .

[45]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[46]  Youness El Hamzaoui,et al.  ANN and ANFIS Models for COP Prediction of a Water Purification Process Integrated to a Heat Transformer with Energy Recycling , 2012 .

[47]  Michael G. Madden,et al.  A neural network approach to predicting stock exchange movements using external factors , 2005, Knowl. Based Syst..

[48]  Esmaeil Hadavandi,et al.  A hybrid intelligent approach for modeling brand choice and constructing a market response simulator , 2013, Knowl. Based Syst..

[49]  Indrajeet Chaubey,et al.  Artificial Neural Network Approach for Mapping Contrasting Tillage Practices , 2010, Remote. Sens..

[50]  Outmane Oubram,et al.  Search for Optimum Operating Conditions for a Water Purification Process Integrated to a Heat Transformer with Energy Recycling using Artificial Neural Network Inverse Solved by Genetic and Particle Swarm Algorithms , 2012 .

[51]  Ehsan Mesbahi,et al.  Artificial neural networks: fundamentals , 2003 .

[52]  B. Kulkarni,et al.  Classification of Indian power coals using K-means clustering and Self Organizing Map neural network , 2011 .

[53]  Arash Ghanbari,et al.  An improved sales forecasting approach by the integration of genetic fuzzy systems and data clustering: Case study of printed circuit board , 2011, Expert Syst. Appl..

[54]  Lance D. Chambers Practical handbook of genetic algorithms , 1995 .

[55]  Sagar Singh Issues in Cultural Tourism Studies, 2nd ed., M.K. Smith. Routledge, London (2009), , 251 (pbk). ISBN: 13: 978-0-415-46712-4 , 2011 .

[56]  M. Bitterman THE EVOLUTION OF INTELLIGENCE. , 1965, Scientific American.

[57]  Randall S. Sexton,et al.  Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem , 2006, Eur. J. Oper. Res..

[58]  Ashu Jain,et al.  Development of effective and efficient rainfall‐runoff models using integration of deterministic, real‐coded genetic algorithms and artificial neural network techniques , 2004 .

[59]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[60]  Konstantinos C. Gryllias,et al.  Rolling element bearing fault detection in industrial environments based on a K-means clustering approach , 2011, Expert Syst. Appl..

[61]  Adam P. Piotrowski,et al.  Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach , 2011 .

[62]  Kimon P. Valavanis,et al.  Surveying stock market forecasting techniques - Part II: Soft computing methods , 2009, Expert Syst. Appl..

[63]  Taher Niknam,et al.  An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis , 2010, Appl. Soft Comput..

[64]  C. L. Wu,et al.  Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis , 2011 .

[65]  Randall S. Sexton,et al.  Toward global optimization of neural networks: A comparison of the genetic algorithm and backpropagation , 1998, Decis. Support Syst..

[66]  M. Saberi,et al.  Improved Estimation of Electricity Demand Function by Integration of Fuzzy System and Data Mining Approach , 2006, 2006 IEEE International Conference on Industrial Technology.

[67]  Ozgur Kisi,et al.  River suspended sediment modelling using a fuzzy logic approach , 2006 .

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

[69]  Fi-John Chang,et al.  Evolutionary artificial neural networks for hydrological systems forecasting , 2009 .

[70]  Amanda J. C. Sharkey,et al.  Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems , 1999 .

[71]  Witold Pedrycz,et al.  Advances in Fuzzy Clustering and its Applications , 2007 .

[72]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[73]  Zbigniew Michalewicz,et al.  Parameter control in evolutionary algorithms , 1999, IEEE Trans. Evol. Comput..

[74]  Young-Oh Kim,et al.  Rainfall‐runoff models using artificial neural networks for ensemble streamflow prediction , 2005 .

[75]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[76]  Mohamed Nasr Allam,et al.  Rainfall‐runoff modelling using artificial neural networks technique: a Blue Nile catchment case study , 2006 .

[77]  E. Fama EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[78]  Mohamed S. Kamel,et al.  Modular neural networks: a survey. , 1999, International journal of neural systems.

[79]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[80]  Kamran Shahanaghi,et al.  Tourist arrival forecasting by evolutionary fuzzy systems. , 2011 .

[81]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[82]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[83]  C. Granger Invited review combining forecasts—twenty years later , 1989 .

[84]  David Horn,et al.  Combined Neural Networks for Time Series Analysis , 1993, NIPS.

[85]  D. Solomatine,et al.  Model trees as an alternative to neural networks in rainfall—runoff modelling , 2003 .

[86]  Lance D. Chambers,et al.  Practical Handbook of Genetic Algorithms: New Frontiers , 1995 .

[87]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[88]  Chao Wu,et al.  Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm , 2011, Knowl. Based Syst..

[89]  Esmaeil Hadavandi,et al.  Hybridization of evolutionary Levenberg-Marquardt neural networks and data pre-processing for stock market prediction , 2012, Knowl. Based Syst..

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

[91]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.