Research Article

Estimation of the Monthly Average Flows of the Kızılırmak River Using Fuzzy Logic Approach

Number: 51 August 31, 2023
EN TR

Estimation of the Monthly Average Flows of the Kızılırmak River Using Fuzzy Logic Approach

Abstract

River flow values are used in the design and operation of hydraulic structures. Determining the correct flow value is important in terms of controlling water movements in the operation of hydraulic structures, irrigation of agricultural lands, hydroelectric production, environmental protection and flood control. In the literature, different methods are used to predict possible river flows using the available data. The fuzzy logic approach is a kind of intelligent system method used in solving problems involving uncertainty. The method has been widely used in the modeling of hydrological data for 2000’s. In this study, the fuzzy logic method was applied to estimate the flow data of Yamula Station on the Kızılırmak River in the Kızılırmak basin, one of the largest basins in Turkey. In addition to these flow station data, the monthly average temperature and monthly total precipitation data of the Kayseri meteorology station, which affects the station flows, were also used for modeling. Three different models were created for the flow estimates. In these models, temperature and precipitation data were selected as input values and river flow data were chosen as output values. In the models, 1982-2012 data of the stations were used. Model output data were tested with data set of 2013, 2014 and 2015. As a result, it has been seen that the fuzzy logic approach gave healthy results when both temperature and precipitation data were used as inputs.

Keywords

Supporting Institution

Selçuk Üniversitesi

References

  1. Anusree, K., & Varghese, K. O. (2016). Streamflow prediction of Karuvannur River Basin using ANFIS, ANN and MNLR models. Procedia Technology, 24, 101-108.
  2. Arıkan, B. B. and E. Kahya, 2019: Homogeneity revisited: Analysis of updated precipitation series in Turkey, Theoretical and Applied Climatology, 135 (1-2), 211-220, DOI: 10.1007/s00704-018-2368-x
  3. Badrzadeh, H., Sarukkalige, R., & Jayawardena, A. W. (2018). Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model. Hydrology Research, 49(1), 27-40.
  4. Bisht, D. C., & Jangid, A. (2011). Discharge modelling using adaptive neuro-fuzzy inference system. International Journal of Advanced Science and Technology, 31(1), 99-114.
  5. Büyükkaracığan, N. (2022). Fuzzy logic applications in civil engineering. İksad Publising House, Ankara.
  6. Chai, Y., Jia, L., & Zhang, Z. (2009). Mamdani model based adaptive neural fuzzy inference system and its application. International Journal of Computer and Information Engineering, 3(3), 663-670.
  7. Chang, F. J., Hu, H. F., & Chen, Y. C. (2001). Counterpropagation fuzzy–neural network for streamflow reconstruction. Hydrological Processes, 15(2), 219-232.
  8. Dawood, M., Rahman, A. U., Mahmood, S., Rahman, G., & Nazir, S. (2021). Assessing the impact of climatic change on discharge in Swat river basin using fuzzy logic model. Arabian Journal of Geosciences, 14(18), 1-16.

Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Early Pub Date

September 12, 2023

Publication Date

August 31, 2023

Submission Date

January 24, 2023

Acceptance Date

August 25, 2023

Published in Issue

Year 2023 Number: 51

APA
Büyükkaracığan, N. (2023). Estimation of the Monthly Average Flows of the Kızılırmak River Using Fuzzy Logic Approach. Avrupa Bilim Ve Teknoloji Dergisi, 51, 368-375. https://doi.org/10.31590/ejosat.1241399

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