Determining Runoff Coefficient For Kalecik Basin in Using Fuzzy Smrgt Method

Fatih Sevgin; Zeynel Fuat  Toprak

doi:10.4025/actascitechnol.v47i1.70976

Authors

Fatih Sevgin Mus Alparslan University https://orcid.org/0000-0002-1984-8162
Zeynel Fuat Toprak Dicle University

DOI:

https://doi.org/10.4025/actascitechnol.v47i1.70976

Keywords:

Runoff coefficient, Fuzzy logic, SMRGT, Kalecik basÄ±n

Abstract

Precipitation causes runoff with significant uncertainty. The rainfall-runoff modeling relationship depends on the runoff coefficient. Many models have been developed with different methods to calculate the runoff coefficient. Black box or fuzzy models can be preferred instead of deterministic methods in uncertain natural events. However, black box methods often do not consider the event's physical aspect. Therefore, in the present study, Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) which base on fuzzy logic was preferred in determining the runoff coefficient since it also reflects the physical cause-effect relationship of the event. By this way, both the hydrological event's uncertainty and physical aspects were addressed. Therefore, it can be used for any basin when the limit values of the variables are expanded. Correctly determining fuzzy sets and fuzzy rule bases are essential points to be considered in fuzzy modeling. According to the literature, SMRGT is the best one to use for this purpose. On the other hand, SMRGT is relatively new. Meteorological, geomorphological, and land use-related characteristics were considered for modeling. The Kalecik Basin's runoff coefficient is found as 0.28 which is lesser than the average of Turkey. The model has 2.28% of MARE.

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References

Alta?, E., Ayd?n, M. C., & Toprak, Z. F. (2018). Modeling water surface profile in open channel flows using Fuzzy SMRGT method. Dicle University Journal of Engineering (DUJE), 9(2), 975–981.

Cleveland, T. G., He, X., Asquith, W. H., & Fang, X. (2006). Instantaneous unit hydrograph evaluation for rainfall-runoff modeling of small watersheds in north and south central Texas. Journal of Irrigation and Drainage Engineering, 132(5), 479–485. https://doi.org/10.1061/(ASCE)0733-9437(2006)132:5(479) DOI: https://doi.org/10.1061/(ASCE)0733-9437(2006)132:5(479)

Gunal, A. Y., & Mehdi, R. (2023). Forecasting the flow coefficient of the river basin using adaptive fuzzy inference system and fuzzy SMRGT method. Journal of Ecological Engineering, 24(7), 96–107. https://doi.org/10.12911/22998993/160331 DOI: https://doi.org/10.12911/22998993/163367

Karakaya, D., Toprak, Z. F. (2018). Classification Water Losses in Water Distribution Networks Using ZFT Algorithm. Su Kaynaklar?, 3(2), 22-30.

Kumar, P., Lohani, A. K., & Nema, A. K. (2017). Rainfall-runoff modeling using MIKE 11 NAM model. Current World Environment, 14(1), 27–34. https://doi.org/10.12944/CWE.14.1.04 DOI: https://doi.org/10.12944/CWE.14.1.05

Merz, R., Blöschl, G., & Parajka, J. (2006). Spatio-temporal variability of event runoff coefficients. Journal of Hydrology, 331(3–4), 591–604. https://doi.org/10.1016/j.jhydrol.2006.06.017 DOI: https://doi.org/10.1016/j.jhydrol.2006.06.008

Mimikou, M., & Rao, A. R. (1983). Regional monthly rainfall-runoff model. Journal of Water Resources Planning and Management, 109(1), 75–93. https://doi.org/10.1061/(ASCE)0733-9496(1983)109:1(75) DOI: https://doi.org/10.1061/(ASCE)0733-9496(1983)109:1(75)

Nayak, T., & Jaiswal, R. (2003). Rainfall-runoff modelling using satellite data and GIS for Bebas River in Madhya Pradesh. Journal of the Institution of Engineers, India. Civil Engineering Division, 84(Mai), 47–50.

O?uz, E. (1993). Natural regions and environs of Turkey. Aegean Geographical Journal, 7(1), 13–41.

Palta, ?., Yurtseven, ?., & Aksay, H. (2019). Rainfall-runoff interactions of Göksu River Basin. Journal of Bartin Faculty of Forestry, 21(3), 860–872. DOI: https://doi.org/10.24011/barofd.560701

Parida, B. P., Moalafhi, D. B., & Kenabatho, P. K. (2006). Forecasting runoff coefficients using ANN for water resources management: The case of Notwane catchment in Eastern Botswana. Physics and Chemistry of the Earth, Parts A/B/C, 31(15–16), 928–934. https://doi.org/10.1016/j.pce.2006.10.004 DOI: https://doi.org/10.1016/j.pce.2006.08.017

Savenije, H. H. G. (1996). The runoff coefficient as the key to moisture recycling. Journal of Hydrology, 176(1–4), 219–225. https://doi.org/10.1016/0022-1694(95)02763-3 DOI: https://doi.org/10.1016/0022-1694(95)02776-9

Sedki, A., Ouazar, D., & Mazoud, E. El. (2009). Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting. Expert Systems with Applications, 36(3), 4523–4527. https://doi.org/10.1016/j.eswa.2008.06.085 DOI: https://doi.org/10.1016/j.eswa.2008.05.024

Toprak, Z. F. (2009). Flow discharge modeling in open canals using a new fuzzy modeling technique (SMRGT). CLEAN–Soil, Air, Water, 37(9), 742–752. https://doi.org/10.1002/clen.200900027 DOI: https://doi.org/10.1002/clen.200900146

Toprak, Z. F. (2019). Bilimsel ara?t?rmalarda yöntem seçimi. In Proceedings of the 2nd International Congress on Engineering and Technology Management (pp. 1–7). Mardin, Turkey.

Toprak, Z. F., Songur, M., & Hamidi, N. (2013). Determination of losses in water-networks using a new fuzzy technique (SMRGT). Global Journal on Technology, 3(2013), 833–840. https://doi.org/10.18844/gjt.v3i2013.1220

Toprak, Z. F., Toprak, A., & Aykaç, Z. (2017). Practical applications of fuzzy SMRGT method. Dicle University Journal of Engineering (DUJE), 8(1), 123–132.

Tsykin, E. (1985). Multiple nonlinear statistical models for runoff simulation and prediction. Journal of Hydrology, 77(1–4), 209–226. https://doi.org/10.1016/0022-1694(85)90212-5 DOI: https://doi.org/10.1016/0022-1694(85)90207-0

Üne?, F., Demirci, M., Zelenakova, M., Çal???c?, M., & Ta?ar, B. (2020). River flow estimation using artificial intelligence and fuzzy techniques. Water, 12(9), 2427. https://doi.org/10.3390/w12092427 DOI: https://doi.org/10.3390/w12092427

Üstün, ?., Üne?, F., Mert, ?., & Karaku?, C. (2020). A comparative study of estimating solar radiation using machine learning approaches: DL, SMRGT, and ANFIS. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44(4), 10322–10345. https://doi.org/10.1080/15567036.2020.1825135 DOI: https://doi.org/10.1080/15567036.2020.1781301

Yalaz, S., & Arife, A. (2016). Fuzzy linear regression for the time series data which is fuzzified with SMRGT method. Süleyman Demirel University Journal of Natural and Applied Sciences, 20(3), 405–413. DOI: https://doi.org/10.19113/sdufbed.49849