Flood-routing modeling with neural network optimized by social-based algorithm

Forecasting and operational routing flood requires accurate forecasts on proper feed time, to be able to issue suitable warnings and take suitable emergency actions. Flood-routing problem is one of the most complicated matters in hydraulics of open channels and river engineering. Flood routing is the process of computing the progressive time and shape of a flood wave at successive points along a river. To get an approximate solution of the flood-routing problem, different techniques are used. This paper describes an approach to train artificial neural network (ANN) using social-based algorithm (SBA). The approach illustrates feed-forward neural network optimization for the flood-routing problem of Kheir Abad River called FF-SBA. To this end, the number and effective time lag of input data in ANN models are initially determined by means of linear correlation between input and output time series; subsequently, the weights of the feed-forward network is optimized by SBA. Optimization algorithms and statistical models like Genetic Algorithm and linear regression are compared to FF-SBA. Compared to the results of optimization algorithms and statistical models, the FF-SBA model for the Kheir Abad River in Iran shows more flexibility and accuracy.

[1]  Anne Johannet,et al.  Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France) , 2011 .

[2]  Mehdi Nikoo,et al.  Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm , 2014, Neural Computing and Applications.

[3]  Pankaj Singh,et al.  Suitability of different neural networks in daily flow forecasting , 2007, Appl. Soft Comput..

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  H Fathian,et al.  RIVER FLOOD ROUTING USING EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS , 2010 .

[6]  Jia Lu,et al.  The practical research on flood forecasting based on artificial neural networks , 2010, Expert Syst. Appl..

[7]  Raymond J. Burby,et al.  River Ecology and Flood Hazard Mitigation , 2003 .

[8]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[9]  Chandranath Chatterjee,et al.  Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs) , 2010 .

[10]  Mehdi Nikoo,et al.  Artificial neural network weights optimization based on social-based algorithm to realize sediment over the river , 2014, Soft Computing.

[11]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[12]  Shahriar Lotfi,et al.  Social-Based Algorithm (SBA) , 2013, Appl. Soft Comput..

[13]  Holger R. Maier,et al.  Input determination for neural network models in water resources applications. Part 1—background and methodology , 2005 .

[14]  Anne Johannet,et al.  Optimization of the generalization capability for rainfall–runoff modeling by neural networks: the case of the Lez aquifer (southern France) , 2012, Environmental Earth Sciences.

[15]  Chen-hua Shen,et al.  Analysis of detrended time-lagged cross-correlation between two nonstationary time series , 2015 .

[16]  Mohammad Ali Ghorbani,et al.  Comparison of three artificial intelligence techniques for discharge routing , 2011 .

[17]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[18]  Gerald R. North.  DATA ANALYSIS | Time Series Analysis , 2003 .

[19]  A. Mangin,et al.  Contribution of correlation and spectral analyses to the regional study of a large karst aquifer (Charente, France) , 1998 .

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

[21]  Ahmad Tahershamsi,et al.  OPTIMIZATION TO IDENTIFY MUSKINGUM MODEL PARAMETERS USING IMPERIALIST COMPETITIVE ALGORITHM , 2011 .

[22]  R. Abrahart,et al.  Flood estimation at ungauged sites using artificial neural networks , 2006 .

[23]  Linda See,et al.  A conceptual and neural network model for real-time flood forecasting of the Tiber River in Rome , 2010 .

[24]  S. Brody,et al.  The Rising Costs of Floods: Examining the Impact of Planning and Development Decisions on Property Damage in Florida , 2007 .

[25]  Ana P. Barros,et al.  Quantitative flood forecasting using multisensor data and neural networks , 2001 .

[26]  Guangtao Fu,et al.  Classified real-time flood forecasting by coupling fuzzy clustering and neural network , 2010 .

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

[28]  Niranjan Pramanik,et al.  Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model , 2010, Comput. Geosci..

[29]  Duc Truong Pham,et al.  Neural Networks for Identification, Prediction and Control , 1995 .

[30]  Ashu Jain,et al.  Hybrid neural network models for hydrologic time series forecasting , 2007, Appl. Soft Comput..