Statistics

    Map

Twitter


Estimation of Groundwater Level Fluctuations Using Neuro-Fuzzy and Support Vector Regression Models

( Vol-5,Issue-12,December 2018 ) OPEN ACCESS
Author(s):

Mustafa DEMİRCİ, Bestami TAŞAR, Yunus Ziya KAYA, Hakan VARÇİN

Keywords:

Ground water level, Neuro-Fuzzy, Support Vector Regression, Kernel, Modeling.

Abstract:

Estimation of Ground Water Level (GWL) is important in the determination of the sustainable use of water resources and Ground Water resources. Groundwater level fluctuations were investigated using the variable of groundwater level, precipitation, temperature. In the present study, GWL estimation studies were conducted via Neuro-Fuzzy (NF), Support Vector Regression with radial basis functions (SVR-RBF) and Support Vector Regression with poly kernel (SVR-PK) models. The daily data of the precipitation, temperature and groundwater level are used which is taken from Minnesota, United States of America. The results were compared with NF and SVR methods. According to this comparison, it was observed that the NF and SVR models gave similar results for observation.

ijaers doi crossref DOI:

10.22161/ijaers.5.12.29

Paper Statistics:
  • Total View : 170
  • Downloads : 96
  • Page No: 206-212
Cite this Article:
Click here to get all Styles of Citation using DOI of the article.
References:

[1] Yilmaz, B., Aras, E., Nacar, S., & Kankal, M. (2018). Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models. Science of The Total Environment, 639, 826-840.
[2] Demirci, M., Üneş, F., Saydemir, S. (2015). Suspended sediment estimation using an artificial intelligence approach. In: Sediment matters. Eds. P. Heininger, J. Cullmann. Springer International Publishing p. 83–95.
[3] Tasar, B., Kaya, Y. Z., Varcin, 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 (IJAERS), 4(12), pp. 79-84.
[4] 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.
[5] Üneş, F. (2010). Dam reservoir level modelıng by neural network approach. A case study, Neural Network World, 4(10), 461–474.
[6] 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(3), 309–318.
[7] Demirci,M. & Unes, F. (2015) “Generalized Regression Neural Networks For Reservoir Level Modeling”, International Journal of Advanced Computational Engineering and Networking, 3, 81-84.
[8] Üneş, F. (2010). Prediction of density flow plunging depth in dam reservoir: An artificial neural network approach”, Clean - Soil, Air, Water, 38, 296 – 308.
[9] Unes, F., Yildirim, S., Cigizoglu, HK., Coskun, H. (2013). Estimation of dam reservoir volume fluctuations using artificial neural network and support vector regression - Journal of Engineering Research.
[10] Unes, F., Gumuscan, F. G., Demirci, M. (2017). Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach, EJENS, Volume 2, Issue 1, pp. 144-148
[11] Demirci, M., Üneş, F., Kaya, Y.Z., Tasar, B., Varcin, H. (2018). Modeling of Dam Reservoir Volume Using Adaptive Neuro Fuzzy Method, Air and Water Components of the Environment Conference, DOI: 10.24193/AWC2018_18
[12] Demirci,M., Unes, F., Akoz, M. S. (2016). Determination of nearshore sandbar crest depth using neural network approach, International Journal of Advanced Engineering Research and Science (IJAERS) Vol-3, Issue-12, Dec- 2016, ISSN: 2349-6495(P) | 2456-1908(O)
[13] ÜNEŞ FATİH,KAYA YUNUS ZİYA,MAMAK MUSTAFA,DEMİRCİ MUSTAFA (2017). Evapotranspiration Estimation Using Support Vector Machines and Hargreaves-Samani Equation for St. Johns, FL, USA. Proccedings of 10th International Conference ”Environmental Engineering”, Doi: 10.3846/enviro.2017.094
[14] Taşar, B., Üneş, F., Demirci, M., & Kaya, Y. Z. Yapay sinir ağları yöntemi kullanılarak buharlaşma miktarı tahmini. DÜMF Mühendislik Dergisi, 9(1), 543-551. Refearnslar ingilizcesi
[15] 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 “.
[16] Kaya, Y. Z., Üneş, F., Demirci, M. Tasar, B., Varcin, H. (2018). GROUNDWATER LEVEL PREDICTION USING ARTIFICIAL NEURAL NETWORK AND M5 TREE MODELS, Air and Water Components of the Environment Conference, DOI: 10.24193/AWC2018_23
[17] Kebir, F. O., Demirci, M., Karaaslan, M., Ünal, E., Dincer, F., & Arat, H. T. (2014). Smart grid on energy efficiency application for wastewater treatment. Environmental Progress & Sustainable Energy, 33(2), 556-563.
[18] 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).
[19] Cansiz, O. F. (2011). Improvements in estimating a fatal accidents model formed by an artificial neural network. Simulation, 87(6), 512-522.
[20] 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.
[21] Cansız Ö. F., Çalışıcı M., Ünsalan K., Erginer İ. (2017). Türkiye İçin Trafik Kaza Sayısı Tahmin Modellerinin Oluşturulması. 2. Uluslararası Mühendislik ve Tasarım Kongresi, Sayfa 615-616.
[22] Cansız Ö. F., Çalışıcı M., Ünsalan K. (2017). Türkiye Karayollarında Meydana Gelen Kazalarda Oluşan Yaralı Sayısı için Tahmin Modellerinin Oluşturulması, 2. Uluslararası Mühendislik ve Tasarım Kongresi, Sayfa:498-499.
[23] 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.
[24] 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).
[25] Cansız Ö. F., Çalışıcı M., Duran D., Ünsalan K. (2017). Marshall Deneyi Sonuçları İçin Geliştirilen Tahmin Modellerinin İncelenmesi, 2. Uluslararası Mühendislik ve Tasarım Kongresi, Sayfa:523-524.
[26] 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.
[27] Mohanty, S., Jha, M. K., Raul, S. K., Panda, R. K., & Sudheer, K. P. (2015). Using artificial neural network approach for simultaneous forecasting of weekly groundwater levels at multiple sites. Water Resources Management, 29(15), 5521-5532.
[28] Gong, Y., Zhang, Y., Lan, S., & Wang, H. (2016). A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee, Florida. Water resources management, 30(1), 375-391.
[29] Guzman, S. M., Paz, J. O., Tagert, M. L. M., & Mercer, A. E. (2018). Evaluation of Seasonally Classified Inputs for the Prediction of Daily Groundwater Levels: NARX Networks Vs Support Vector Machines. Environmental Modeling & Assessment, 1-12.
[30] Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
[31] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
[32] Vapnik, V. (1995). The nature of statistical learning theory Springer New York
[33] Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE transactions on neural networks, 10(5), 988-999.
[34] Haykin, S. (1999). Multilayer perceptrons. Neural networks: a comprehensive foundation, 2, 135-155.
[35] Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification.