<

This Article Statistics
Viewed : 1825 Downloaded : 1329


 

Artificial Neural Networks Method for Prediction of Rainfall-Runoff Relation: Regional Practice

Fatih Üneş*, Levent Keskin, Mustafa Demirci

Abstract

Rainfall and runoff relation is very important on efficient use of water resources and prevention of disasters. Nowadays, different methods of artificial intelligent techniques are applied to determine the rainfall-runoff relations. Artificial Neural Networks (ANN) is used for the present study. Also, Classical methods such as Multiple Linear Regression (MLR) are used. In this study, the data obtained from USA Waltham Massachusetts Stony Brook Reservoir basin was taken. 731 daily data of rainfall, runoff, and temperature were used to generate input data in the Multi Linear Regression and Feed-Forward Back-Propagation Artificial Neural Network models. The results obtained were compared with the actual results.

Keywords

Rainfall, Runoff, Artificial Intelligence, Artificial Neural Networks, Multiple Linear Regressions

Download full text   |   How to Cite   |   Download XML Files

References
  • Cansız Ö. F. (2007). Enerji Politikalarının Ulaştırma Sistemlerinin Optimizasyonu İle Geliştirilmesi ve Uygulamadan Elde Edilen Getirilerin Ortaya Konması, Doktora Tezi, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, Türkiye (in Turkish).
  • Cansiz, O. F. (2011). Improvements in estimating a fatal accidents model formed by an artificial neural network. Simulation, 87(6), 512-522.
  • Cansiz, O. F., Calisici, M., & Miroglu, M. M. (2009, December). Use of artificial neural network to estimate number of persons fatally injured in motor vehicle accidents. In Proceedings of the 3rd International Conference on Applied Mathematics, Simulation, Modelling, Circuits, Systems and Signals (pp. 136-142). World Scientific and Engineering Academy and Society (WSEAS).
  • Cansiz, O. F., & Easa, S. M. (2011). Using artificial neural network to predict collisions on horizontal tangents of 3D two-lane highways. International Journal of Engineering and Applied Sciences, 7(1), 47-56.
  • Cansız Ö. F., Çalışıcı M., Ünsalan K., Erginer İ. (2017a). Türkiye İçin Trafik Kaza Sayısı Tahmin Modellerinin Oluşturulması. 2. Uluslararası Mühendislik ve Tasarım Kongresi, Sayfa 615-616 (in Turkish).
  • Cansız Ö. F., Çalışıcı M., Ünsalan K. (2017b). Türkiye Karayollarında Meydana Gelen Kazalarda Oluşan Yaralı Sayısı için Tahmin Modellerinin Oluşturulması, 2. Uluslararası Mühendislik ve Tasarım Kongresi, Sayfa:498-499 (in Turkish).
  • Cansız Ö. F., Çalışıcı M., Duran D., Ünsalan K. (2017c). Marshall Deneyi Sonuçları İçin Geliştirilen Tahmin Modellerinin İncelenmesi, 2. Uluslararası Mühendislik ve Tasarım Kongresi, Sayfa:523-524 (in Turkish).
  • Cansız, Ö. F., Askar, D. D. (2018). Developing Multi Linear Regression Models for Estimation of Marshall Stability, International Journal of Advanced Engineering Research and Science (IJAERS), Vol-5, Issue-6. https://dx.doi.org/10.22161/ijaers.5.6.10 ISSN: 2349-6495(P) | 2456-1908(O).
  • Dogan A., Cansiz O. F., Unsalan K., Karaca N. (2017). Investigation of Multi Linear Regression Methods on Estimation of Free Vibration Analysis of Laminated Composite Shallow Shells, International Journal of Advanced Engineering Research and Science (ISSN : 2349-6495(P) | 2456-1908(O)),4(12), 114-120.
  • Dal, K., Cansiz, O. F., Ornek, M., & Turedi, Y. (2019). Prediction of footing settlements with geogrid reinforcement and eccentricity. Geosynthetics International, 1-39.
  • Demirci, M., & Baltaci, A. (2013). Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches. Neural Computing and Applications, 23(1), 145-151.
  • Demirci, M., Kaya & Y.Z. (2019). Estimation of Keban Dam Reservoir Level in Turkey Using Artificial Neural Network and Support Vector Machines. 2019 ”Air and Water – Components of the Environment” Conference Proceedings, Cluj-Napoca, Romania, p. 197-206, DOI: 10.24193/AWC2019_20.
  • Demirci, M., Taşar, B., Kaya, Y. Z, & Varçin, H. (2018a). Estimation of Groundwater Level Fluctuations Using Neuro-Fuzzy and Support Vector Regression Models. International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)),5(12), 206-212. http://dx.doi.org/10.22161/ijaers.5.12.29
  • Demirci, M., Unes, F., Kaya, Y. Z., Mamak, M., Tasar, B., & Ispir, E. (2017, March). Estimation of groundwater level using artificial neural networks: a case study of Hatay-Turkey. In 10th International Conference „Environmental Engineering “.
  • Demirci, M., Unes, F., Kaya, Y. Z., Tasar, B., & Varcin, H. (2018b). MODELING OF DAM RESERVOIR VOLUME USING ADAPTIVE NEURO FUZZY METHOD. Aerul si Apa. Componente ale Mediului, 145-152.
  • Demirci, M., Üneş, F., & Körlü, S. (2019) Modeling of groundwater level using artificial intelligence techniques: a case study of Reyhanlı region in Turkey. Applied Ecology and Env. Research 17(2):2651-2663. http://dx.doi.org/10.15666/aeer/1702_26512663
  • Demirci, M., Üneş, F., & Aköz, M. S. (2015). Prediction of cross-shore sandbar volumes using neural network approach. Journal of Marine Science and Technology, 20(1), 171-179.
  • Demirci, M., Üneş, F., & Saydemir, S. (2015). Suspended sediment estimation using an artificial intelligence approach. In Sediment Matters (pp. 83-95). Springer, Cham.
  • Ergezer, H., Dikmen, M., & Özdemir, E. (2003). Yapay sinir ağları ve tanıma sistemleri. Pivolka, 2(6), 14-17 (in Turkish).
  • Fernando, D., & Jayawardena, A.W. (1998). Runoff Forecasting using RBF Networks with OLS Algorithm. Journal of Hydrologic Engineering, 3(3) 203-209.
  • Hsu, K., Gupta, H., & Sorooshian, S. (1995). Artificial neural network modelling of the rainfall-runoff process. Water Resources Research, 31(10), 2517- 2530.
  • Kaya, Y. Z., Üneş, F., Demirci, M., Taşar, B., & Varçin, H. (2018). Groundwater Level Prediction Using Artificial Neural Network and M5 Tree Models. Aerul si Apa. Componente ale Mediului, 195-201.
  • Kaya, Y.Z., Taşar, B. (2019) Evapotranspiration Calculation for South Carolina, USA and Creation Different ANFIS Models for ET Estimation. 2019 ”Air and Water – Components of the Environment” Conference Proceedings, Cluj-Napoca, Romania, p. 217-224, DOI: 10.24193/AWC2019_22.
  • Mason, J.C., Price, R.K., & Tem‘me, A. (1996). A Neural network model of rainfall-runoff using radial basis functions. Journal of Hydraulic Research, 34(4), 537- 548.
  • McCulloch, S.W., & Pitts, H.W. (1943). A logical calculus of the ideas immanent in neural net. Bulletin of Mathematical Biophysics, Volume 5.
  • Minns, A.W., & Hall, M.J. (1996). Artificial Neural Networks as Rainfall Runoff Models. Hydrological Sciences Journal, 41(3), 399-417.
  • Tașar, B., Unes, F., Varcin, H. (2019). Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines. 2019 ”Air and Water – Components of the Environment” Conference Proceedings, Cluj-Napoca, Romania, p. 237-246, DOI: 10.24193/AWC2019_24.
  • Taşar, B., Kaya, Y. Z., Varçin, H., Üneş, F., & Demirci, M. (2017). Forecasting of suspended sediment in rivers using artificial neural networks approach. International Journal of Advanced Engineering Research and Science, 4(12).
  • Taşar, B., Üneş, F., Demirci, M., & Kaya, Y. Z. (2018). Yapay sinir ağları yöntemi kullanılarak buharlaşma miktarı tahmini. DÜMF Mühendislik Dergisi, 9(1), 543-551 (in Turkish).
  • Unes, F., Gumuscan F.G., Demirci M. (2017). Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach. EJENS, 2 (1), 144-148.
  • Ünes, F. (2010). Prediction of density flow plunging depth in dam reservoirs: an artificial neural network approach. Clean–Soil, Air, Water, 38(3), 296-308.
  • Üneş, F. & Demirci, M. (2015). Generalized Regression Neural Networks For Reservoir Level Modeling. International Journal of Advanced Computational Engineering and Networking , 3, 81-84.
  • Üneş, F., Demirci, M. & Kişi, Ö. (2015). Prediction of millers ferry dam reservoir level in USA using artificial neural network. Periodica Polytechnica Civil Engineering, 59, 309–318.
  • Üneş, F., Bölük, O., Kaya, Y. Z., Taşar, B., &Varçin, H. (2018b). Estimation of Rainfall-Runoff Relationship Using Artificial Neural Network Models for Muskegon Basin. International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)),5(12), 198-205. http://dx.doi.org/10.22161/ijaers.5.12.28
  • Üneş, F., Demirci, M., Mertcan, Z., Taşar, B., Varçin, H., Ziya, Y. (2018a). Determination of Groundwater Level Fluctuations by Artificial Neural Networks. Natural and Engineering Sciences, 3(3), Supplement, 35-42.
  • Üneş, F., Demirci, M., Taşar, B., Kaya, Y.Z., & Varçin H. (2019a). Estimating Dam Reservoir Level Fluctuations Using Data-Driven Techniques. Pol. J. Environ. Stud. Vol. 28, No. 5 (2019), 1-12. DOI: 10.15244/pjoes/93923
  • Üneş, F., Demirci, M., Taşar, B., Kaya, Y.Z., & Varçin, H., (2019b). Modeling of dam reservoir volume using generalized regression neural network, support vector machines and M5 decision tree models. Applied Ecology and Environmental Research. 17(3), 7043-7055.
  • Üneş, F., Doğan, S., Taşar, B., Kaya, Y., Demirci, M. (2018c). The Evaluation and Comparison of Daily Reference Evapotranspiration with ANN and Empirical Methods. Natural and Engineering Sciences, 3(3), Supplement, 54-64.