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

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Abstract

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.

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Notes

  1. Input, 3 hidden layers, output.

References

  • Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE congress on evolutionary computation, pp 4661–4666

  • Birkland TA, Burby RJ, Conrad D, Cortner H, Michener WK (2003) River ecology and flood hazard mitigation. Nat Hazards Rev 4(1):46–54

    Article  Google Scholar 

  • Bowden GJ, Dandy GC, Maier HR (2005) Input determination for neural network models in water resources applications: part 1—background and methodology. J Hydrol 301(1–4):75–92

    Article  Google Scholar 

  • Brody SD, Zahran S, Maghelal P, Grover H, Highfield WE (2007) The rising costs of floods: examining the impact of planning and development decisions on property damage in Florida. J Am Plann As 73(3):330–345

    Article  Google Scholar 

  • Chan NW (2012) Impacts of disasters and disasters risk management in Malaysia: the case of floods. In: Sawada Y, Oum S (eds) Economic and welfare impacts of disasters in East Asia and Policy Responses. ERIA Research Project Report 2011-8, ERIA, Jakarta, pp 503–551

  • Chen Y-h, Chang F-J (2009) Evolutionary artificial neural networks for hydrological systems forecasting. J Hydrol 367(1–2):125–137

    Article  Google Scholar 

  • Dawson CW, Abrahart RJ, Shamseldin AY, Wilby RL (2006) Flood estimation at ungauged sites using artificial neural networks. J Hydrol 319(1–4):391–409

    Article  Google Scholar 

  • Fathian H, Nikoo M, Nikoo M (2011) River flood routing using evolutionary artificial neural networks. Water Eng 3(5):13–23

    Google Scholar 

  • Feng L-H, Lu J (2010) The practical research on flood forecasting based on artificial neural networks. Expert Syst Appl 37:2974–2977

    Article  Google Scholar 

  • Floater G, Bujak A, Hamill G, Lee M (2014) RAMSES PROJECT, WP 5: development of a cost assessment framework for adaptation, D5.1: review of climate change losses and adaptation costs for case studies. Technological Development and Demonstration under Grant Agreement No. 308497 (Project RAMSES), p 22

  • Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Comput 4:1–58

    Article  Google Scholar 

  • Hornick K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  • Jain A, Kumar AM (2007) Hybrid neural network models for hydrologic time series forecasting. Appl Soft Comput 7(2):585–592

    Article  Google Scholar 

  • Khatibi R, Ghorbani MA, Kashani MH, Kisi O (2011) Comparison of three artificial intelligence techniques for discharge routing. J Hydrol 403(3–4):201–212

    Article  Google Scholar 

  • Kim G, Barros AP (2001) Quantitative flood forecasting using multisensor data and neural network. J Hydrol 246(1–4):45–62

    Article  Google Scholar 

  • Kong-A-Siou L, Johannet A, Borrell Estupina V, Pistre S (2011) Optimization of the generalization capability for rainfall-runoff modeling by neural networks: the case of the Lez aquifer (southern France). Environ Earth Sci 65:2365–2375

    Article  Google Scholar 

  • Larocque M, Mangin A, Razack M, Banton O (1998) Contribution of correlation and spectral analyses to the regional study of a large karst aquifer (Charente, France). J Hydrol 205(3–4):217–231

    Article  Google Scholar 

  • Lippmann RP (1987) An introduction to computing with neural nets. IEEE ASSP Mag 4–22

  • Napolitano G, See L, Calvo B, Savi F, Heppenstall A (2010) A conceptual and neural network model for real-time flood forecasting of the Tiber River in Rome. Phys Chem Earth Pt A/B/C 35(3–5):187–194

    Article  Google Scholar 

  • North GR (2003) Data analysis. Time series analysis. Encycl of Atmos Sci 621–624

  • Panda RK, Pramanik N, Bala B (2010) Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model. Comput Geosci 36:735–745

    Article  Google Scholar 

  • Pham DT, Liu X (1995) Neural networks for identification, prediction and control. Springer, London

    Book  Google Scholar 

  • Pham DT, Koç E, Ghanbarzadeh A, Otri S (2006) Optimization of the weights of multi-layered perceptrons using the bees algorithm. In: Proceedings of 5th international symposium on intelligent manufacturing systems, pp 38–46

  • Ramezani F, Lotfi S (2013) Social-based algorithm (SBA). Appl Soft Comput 13(5):2837–2856

    Article  Google Scholar 

  • Ramezani F, Nikoo M, Nikoo M (2015) Artificial neural network weights optimization based on social-based algorithm to realize sediment over the river. Soft Comput 19:375–387. doi:10.1007/s00500-014-1258-0

    Article  Google Scholar 

  • Ren M, Wang B, Liang Q, Fu G (2010) Classified real-time flood forecasting by coupling fuzzy clustering and neural network. Int J Sediment Res 25(2):134–148

    Article  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing: explorations in the microstructure of cognition. Volume 1: foundations. MIT Press, Cambridge

    Google Scholar 

  • Sadowski L, Nikoo M (2014) Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm. Neural Comput Appl 25(7–8):1627–1638

    Article  Google Scholar 

  • Shen C (2015) Analysis of detrended time-lagged cross-correlation between two nonstationary time series. Phys Lett A 379(7):680–687. doi:10.1016/j.physleta.2014.12.036

    Article  Google Scholar 

  • Singh P, Deo MC (2007) Suitability of different neural networks in daily flow forecasting. Appl Soft Comput 7:968–978

    Article  Google Scholar 

  • Siou LKA, Johannet A, Borrell V, Pistre S (2011) Complexity selection of a neural network model for karst flood forecasting: the case of the Lez Basin (southern France). J Hydrol 403(3–4):367–380

    Article  Google Scholar 

  • Tahershamsi A, Sheikholeslami R (2011) Optimization to identify muskingum model parameters using imperialist competitive algorithm. Int J Optim Civil Eng 3:475–484

    Google Scholar 

  • Tiwari MK, Chatterjee C (2010) Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs). J Hydrol 382(1–4):20–33

    Article  Google Scholar 

  • Wright JM (2000) A report by the Association of State Floodplain Managers. The Nation’s Responses to Flood Disasters: A Historical Account, Association of State Floodplain Managers, Madison

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran

    Mehdi Nikoo

  2. Young Researchers Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Fatemeh Ramezani

  3. Faculty of Civil Engineering, University of J.J. Strossmayer, Drinska 16A, 31000, Osijek, Croatia

    Marijana Hadzima-Nyarko

  4. Faculty of Electrical Engineering, University J.J. Strossmayer in Osijek, K. Trpimira 2B, 31000, Osijek, Croatia

    Emmanuel Karlo Nyarko

  5. SAMA Technical and Vocational Training College, Islamic Azad University , Ahvaz Branch, Ahvaz, Iran

    Mohammad Nikoo

Authors
  1. Mehdi Nikoo
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  2. Fatemeh Ramezani
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  3. Marijana Hadzima-Nyarko
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  4. Emmanuel Karlo Nyarko
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  5. Mohammad Nikoo
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Corresponding author

Correspondence to Mehdi Nikoo.

Appendix: Procedure SBA

Appendix: Procedure SBA

Step 1 :

Initialization;

Generate some random people;

Randomly allocate remain people to others countries;

Select more powerful leaders as the empires;

Step 2 :

Evolutionary Algorithm

Roulette Wheel Selection;

Mutation;

Replacement;

Step 3 :

Imperialist Competitive Algorithm

People Assimilation; Move the people of each country toward their relevant leaders.

People Revolutionary;

Countries Assimilation; Move the leaders of each country toward their empires and move the people of each country as the same as their leaders.

Countries Revolutionary;

Imperialistic Competition; Pick the weakest country from the weakest empire and give it to the empire that has the most likelihood to possess it.

Elimination; Eliminate the powerless empires.

Step 4 :

Terminating Criterion Control; Repeat Steps 2–3 until a terminating criterion is satisfied.

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Nikoo, M., Ramezani, F., Hadzima-Nyarko, M. et al. Flood-routing modeling with neural network optimized by social-based algorithm. Nat Hazards 82, 1–24 (2016). https://doi.org/10.1007/s11069-016-2176-5

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  • DOI: https://doi.org/10.1007/s11069-016-2176-5

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