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

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  • Mustafa Erkan Turan

    (Celal Bayar University)

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.

Suggested Citation

  • Mustafa Erkan Turan, 2016. "Fuzzy Systems Tuned By Swarm Based Optimization Algorithms for Predicting Stream flow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4345-4362, September.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:12:d:10.1007_s11269-016-1424-5
    DOI: 10.1007/s11269-016-1424-5
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    References listed on IDEAS

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    1. Mohd Nadhir Ab Wahab & Samia Nefti-Meziani & Adham Atyabi, 2015. "A Comprehensive Review of Swarm Optimization Algorithms," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-36, May.
    2. Mustafa Turan & Mehmet Yurdusev, 2014. "Predicting Monthly River Flows by Genetic Fuzzy Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4685-4697, October.
    3. Ozgur Kisi & Mohammad Zounemat-Kermani, 2014. "Comparison of Two Different Adaptive Neuro-Fuzzy Inference Systems in Modelling Daily Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2655-2675, July.
    4. Maryam Shafaei & Ozgur Kisi, 2016. "Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 79-97, January.
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