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Fuzzy Systems Tuned By Swarm Based Optimization Algorithms for Predicting Stream flow

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Abstract

River flow prediction is an important phenomenon in water resources for which different methods and perspective have been used. Using fuzzy system with black box perspective is one of them. Fuzzy systems have some parameters and properties that have to be determined. This is an optimization problem that can be solved by swarm optimization techniques among several techniques. Swarm optimization are developed by inspiring from the behavior of the animals living as swarm. The study presents two achievements fuzzy system that tuned by swarm optimization algorithms can be used for prediction of monthly mean streamflow and which swarm optimization algorithm is better than the others for tuning fuzzy systems. Three swarm optimization algorithms, hunter search, firefly, artificial bee colony are used in this study. These algorithms are compared with mean performance values and convergence speed. Monthly streamflow data of three stream gauging stations in Susurluk Basin are used for the case study. The results show, swarm optimization algorithms can be used for prediction of monthly mean streamflow and ABC algorithm has better performance values than other optimization algorithms.

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References

  • Ab Wahab MN, Nefti-Meziani S, Atyabi A (2015) A comprehensive review of swarm optimization algorithms. Plos One 10(5), e0122827. doi:10.1371/journal.pone.0122827

    Article  Google Scholar 

  • Bouzaida S, Sakly A, M’Sahli F (2014) Extracting TSK-type neuro-fuzzy model using the hunting search algorithm. Int J Gen Syst 43(1):32–43

    Article  Google Scholar 

  • Cordon O, Herrera F (1997) Evolutionary design of TSK fuzzy rule-based systems using (μ,λ)-evolution strategies. Fuzzy Systems, 1997., Proceedings of the Sixth IEEE International Conference on, Barcelona, 1997, 509–514 vol.1. doi: 10.1109/FUZZY.1997.616419

  • Danandeh Mehr A, Kahya E, Şahin A, Nazemosadat MJ (2015) Successive-station monthly streamflow prediction using different artificial neural network algorithms. Int J Environ Sci Technol 12(7):2191–2200

    Article  Google Scholar 

  • Dastorani MT, Moghadamnia A, Piri J, Rico-Ramirez M (2010) Application of ANN and ANFIS models for reconstructing missing flow data. Environ Monit Assess 166(1):421–434

    Article  Google Scholar 

  • Dogan E (2014) Solving design optimization problems via hunting search algorithm with levy flights. Struct Eng Mech 52(2):351–368

    Article  Google Scholar 

  • Fırat M, Turan ME (2009) Monthly river flow forecasting by an adaptive neuro-fuzzy inference system. Water Environ J 24(2):116–125

    Article  Google Scholar 

  • Habbi H, Boudouaoui Y, Karaboga D, Ozturk C (2015) Self-generated fuzzy systems design using artificial bee colony optimization. Inf Sci 295:145–159

    Article  Google Scholar 

  • Karaboğa D (2005) An Idea Based on Honey Bee Swarm For Numerical Optimization,Technical Report-TR06. Kayseri: Erciyes University, Engineering Faculty, Computer Engineering Department

  • Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  • Kisi O, Zounemat-Kermani M (2014) Comparison of Two different adaptive neuro-fuzzy inference systems in modelling daily reference evapotranspiration. Water Resour Manag 28(9):2655–2675

    Article  Google Scholar 

  • Mohanty D (2016) Application of firefly algorithm for design optimization of a shell and tube heat exchanger from economic point of view. Int J Therm Sci 102:228–238

    Article  Google Scholar 

  • Nhu HN, Nitsuwat S, Sodanil M (2013) Prediction of Stock Price Using An Adaptive Neuro-Fuzzy Inference System Trained by Firefly Algorithm. 17th International Computer Science and Engineering Conference (ICSEC), 302–307. Nakhon Pathom, THAILAND

  • Oftadeh R, Mahjoob MJ, Shariatpanahi M (2010) A novel meta-heuristic optimization algorithm inspired by group hunting of animals:hunting search. Comput Math Appl 60(7):2087–2098

    Article  Google Scholar 

  • Ozturk C, Hancer E, Karaboga D (2015) Improved clustering criterion for image clustering with artificial bee colony algorithm. Pattern Anal Applic 18(3):587–599

    Article  Google Scholar 

  • Shafaei M, Kisi O (2016) Lake level forecasting using wavelet-SVR, wavelet-ANFIS and wavelet-ARMA conjunction models. Water Resour Manag 30(1):79–97

    Article  Google Scholar 

  • SHW (State Hydraulic Works), 2015. 2014 Annual Report, Ankara

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its aplications to modeling and control. IEEE Trans Syst Man Cybern 15(1):116–132

    Article  Google Scholar 

  • Turan ME, Yurdusev MA (2009) River flow estimation from upstream flow records by artificial intelligence methods. Journal of Hydrology, 71–77

  • Turan ME, Yurdusev MA (2014) Predicting monthly river flows by genetic fuzzy systems. Water Resour Manag 28(13):4685–4697

    Article  Google Scholar 

  • Yang XS, He X (2013) Firefly algorithm: recent advances and applications. Int J Swarm Intell 1:36–50

    Article  Google Scholar 

  • Yen J, Langari R (1999) Fuzzy Logic: Intelligence, Control, And Information. Pearson Education

Download references

Acknowledgment

I would like to thank Dr. Erkan DOĞAN for his valuable suggestions.

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Authors and Affiliations

  1. Faculty of Engineering, Civil Engineering Department, Celal Bayar University, Muradiye, Manisa, Turkey

    Mustafa Erkan Turan

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  1. Mustafa Erkan Turan
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Correspondence to Mustafa Erkan Turan.

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Turan, M.E. Fuzzy Systems Tuned By Swarm Based Optimization Algorithms for Predicting Stream flow. Water Resour Manage 30, 4345–4362 (2016). https://doi.org/10.1007/s11269-016-1424-5

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  • DOI: https://doi.org/10.1007/s11269-016-1424-5

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