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Adaptive Management of the Storage Function for a Large Reservoir Using Learned Fuzzy Models

  • WATER RESOURCES AND THE REGIME OF WATER BODIES
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Abstract

A single study investigates the possibility of using adaptive control of the reservoir Vranov Reservoir situated on the Dyje River. The control algorithm uses a fuzzy model that approximates the I/O relationships contained in the behaviour matrix of the target reservoir, constructed using the differential evolution optimisation method. Recurring predictions of water inflows into the reservoir are constructed using a fuzzy model, which is based on the idea of similarity of the course of a real series of average monthly flows during the year. After calibration of the control and predictive models, the whole control is tested for the period 2004–2018. The results obtained by the discussed models are compared with the results obtained by dispatching graphs. The results of adaptive control show that the method is very suitable for driving during long low-water periods. In periods with plentiful water, the results barely differ.

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REFERENCES

  1. Amnatsan, S., Yoshikawa, S., and Kanae S., Improved forecasting of extreme monthly reservoir inflow using an analogue-based forecasting method: A case study of the Sirikit Dam in Thailand, Water (Switzerland), 2018, vol. 10, no. 11.

  2. Annual report on the hydrometeorological situation in the Czech Republic 2017–2018, Český hydrometeorologický ústav, Na Šabatce 2050/17, 143 06 Praha 4, http://portal.chmi.cz/files/portal/docs/hydro/sucho/ Zpravy/ROK_2018.pdf

  3. Bezdec, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981. https://doi.org/10.1007/978-1-4757-0450-1

  4. Chaves, P., Tsukatani, T., and Kojiri, T., Operation of storage reservoir for water quality by using optimization and artificial intelligence techniques, Mathematics and Computers in Simulation, 2004, vol: 67, no. 4, pp. 419–432.

  5. Chaves, P. and Kojiri. T., Deriving reservoir operational strategies considering water quantity and quality objectives by stochastic fuzzy neural networks, Adv. Water Resour., 2007, vol. 30, pp. 1329–1341. https://doi.org/10.1016/j.advwatres.2006.11.011

    Article  Google Scholar 

  6. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, Chichester, UK: John Wiley and Sons, 2001.

    Google Scholar 

  7. Handling order for VD Vranov, Prague, 2011, pp. 1–77.

  8. Haupt, R.L. and Haupt, S.E., Practical Genetic Algorithms, New York: John Wiley and Sons, 1998.

    Google Scholar 

  9. Holland, J.H., Adaptation in Natural and Artificial Systems: An introductory Analysis with Applications to Biology, Control and Artificial Intelligence, MA, USA: MIT Press, Cambridge, 1975.

    Google Scholar 

  10. Janal, P. and Stary, M., Fuzzy model used for the prediction of a state of emergency for a river basin in the case of a flash flood—PART 2, J. Hydrol. Hydromech., 2012, vol. 60, no. 3, pp. 162–173.

    Article  Google Scholar 

  11. Kasparek, L., Estimation of the volume of reservoir needed to compensate for the decrease in inflow due to climate change, Ministry of Agriculture, VÚV Prague, Prague, 2005.

    Google Scholar 

  12. Kozel, T. and Stary, M., Adaptive stochastic management of the storage function for a large open reservoir using an artificial intelligence method, J. Hydrol. Hydromech., 2019, vol. 64, no. 4, pp. 314–321.

    Article  Google Scholar 

  13. Marton, D., Mensík, P., and Stary, M., Using Predictive Model for Strategic Control of Multi-reservoir System Storage Capacity, Procedia Engineering, Amsterdam: Elsevier Science Publishers, 2015. https://doi.org/10.1016/j.proeng.2015.08.991

  14. Price, K., Storn, R., and Lampinen, J., Differential Evolution: A Practical Approach to Global Optimization, Berlin: Springer-Verlag., 2006.

    Google Scholar 

  15. Rosenblatt, F., The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Psychol. Rev., 1958, vol. 65, no. 6, pp. 386–408.

    Article  Google Scholar 

  16. Shannon, C.E., The Bell System, Technical Journal, 1948, vol. 27, pp. 379–423.

    Article  Google Scholar 

  17. Storn R. and Price, K., Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces, J. Glob. Optim., 1997, vol. 11, pp. 341–359. https://doi.org/10.1023/A:1008202821328

    Article  Google Scholar 

  18. Sugeno, M., Fuzzy Measures and Fuzzy Integrals, in Fuzzy Automata and Decision Processes, Gupta, M.M., Saridis, G.N., and Ganies, B.R., New York, North-Holland, 1977, pp. 89–102.

  19. Tagaki, H. and Sugeno, M., Fuzzy identification of systems and its applications to modelling and control, in IEEE Trans. On Systems, Man and Cybern., 1985, pp. 116–132.

    Google Scholar 

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ACKNOWLEDGMENTS

The article was supported by grant Possibilities of improving water quality in watercourses at low water levels FAST-S-21-7482.

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Correspondence to Kozel Tomas or Stary Milos.

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Tomas, K., Milos, S. Adaptive Management of the Storage Function for a Large Reservoir Using Learned Fuzzy Models. Water Resour 48, 532–543 (2021). https://doi.org/10.1134/S0097807821040084

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  • DOI: https://doi.org/10.1134/S0097807821040084

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