Impact of climate change on the daily water level fluctuation of Lake Sapanca

Fresenius Environmental Bulletin

Volumn 22, Issue 7, 2013, Pages 1895-1903

  Sonmez, Osman ^a   Dogan, Emrah ^a   Ceribasi, Gokmen ^a   Demir, Sumeyra ^b  

^a SAKARYA UNIVERSITY   (Turkey)

^b Sakarya Water and Sewerage Administration   (Turkey)

Author keywords

Artificial neural networks; Evaporation; Lake level; Lake sapanca; Rainfall; Relative humidity; Temperature; Wind speed

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLIMATE CHANGE; CLIMATE EFFECT; EVAPORATION; HYDROLOGICAL MODELING; LAKE LEVEL; RAINFALL; RELATIVE HUMIDITY; WATER LEVEL; WATER RESOURCE; WIND VELOCITY;

SAPANCA LAKE; TURKEY;

EID: 84884652260     PISSN: 10184619     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (30)

1. [I] Trajkovic, S., Todorovic, B., Stankovic, M., (2003), Forecasting Reference Evapotranspiration by Artificial Neural Networks, ASCE J. Irrig. Drain, Eng., 129,6,454-457. (Pubitemid 37519538)
2. Sudheer K.P., Gosain A.K., Rangan D.M., Saheb S.M., (2002), Modelling Evaporation Using an Artificial Neural Network Algorithm, Hydrological Processes, 16,3189-3202. (Pubitemid 35307774)
3. Kisi, O., (2006), Generalized regression neural networks for evapotranspiration modeling. Hydro. Sci. J., 51, 6, 1092-1105.
4. Kisi, O., (2006), Evapotranspiration estimation using feedforward neural networks, Nordic Hydro, 36, 1,49-64.
5. Dawson, W.C., Wilby. R., (1998), An artificial neural network approach to rainfall-runoff modeling, Hydrological Sciences Journal, 43,1,47-66.
6. Solomatine, D.P., Dulal, K.N., (2003), Model trees as an alternative to neural networks in rainfall-runoff modeling. Hydrological Science Journal, 48,3, 399-411.
7. Raman, H., and Sunilkumar, N., (1995), Multivariate Modelling of Water Resources Time Series Using Artificial Neural Networks, Hydro. Sci. J., 40, 2, 145-163. (Pubitemid 26436381)
8. Zealand, C. M., Burn, D. H. and Simonovic, S. P., (1999), Short Term Stream Flow Forecasting Using Artificial Neural Networks, Hydro., 214,32-48. (Pubitemid 29087395)
9. Sivakumar, B., Jayawardena, A.W. and Fernando, T.M.K.G., (2002), River Flow Forecasting: Use of Phase Space Reconstruction and Artificial Neural Networks Approaches, Hydro. 265, 225-245. (Pubitemid 35028775)
10. Cigizoglu, H. K., (2003), Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydro. Sci. J., 48,3,349-361.
11. [II] Saad, M., Bigras, P., Turgeon, A. and Duquette, R., (1996), Fuzzy !-earning decomposition for the scheduling of hydroelectric power systems, Water Res., 32, 1, 179-186.
12. Jain, S.K., Das, D. and Srivastava, D.K., (1999). Application of ANN for reservoir inflow prediction and operation, Journal of Water Resources Planning and Management, 125, 5, 263-271.
13. Kisi, O., (2005), Suspended sediment estimation using neuro-fuzzy and neural network approaches, Hydro. Sci. J., 50,4,683-696.
14. Cigizoglu, H.K. and Kisi, O., (2006), Methods to improve the neural network performance in suspended sediment estimation, J. Hydro., 317,221-238.
15. Altunkaynak, A., (2007), Forecasting surface water level fluctuations of lake van by artificial neural networks, Water Resources Management, 21,399-408.
16. Ondimu, S. and Murase, H., (2007), Reservoir Level Forecasting Using Neural Networks: Lake Naivasha. Biosystems Engineering, 96, 1, 135-138. (Pubitemid 44895410)
17. Kisi O, (2009). Neural network and wavelet conjunction model for modelling monthly level fluctuations in Turkey, Hydro. Process, 23,2081-2092.
18. Kucukkilavuz, E., (2009), Global Warming Effects on Water Resources: The Case Study of Turkey, Harran University Institute of Social Sciences, Department of Economics, Post Graduate Thesis, (in Turkish).
19. Intergovernmental Panel on Climate Change (IPCC), (1992), The Supplementary Report to the IPCC Scientific Assessment, New York, Cambridge University Pres.
20. Ceylan, M.A., (1990), Hydrological Study of Lake Sapanca, Istanbul University, Institute of Social Sciences, Istanbul, Turkey.
21. Guler, M., (1999), Lake Sapanca and Sapanca Basins Natural Resources Using, Istanbul University, Institute of Science, Istanbul, Turkey.
22. Gokce, H., (2005), Modelling of Evaporation in Lake Sapanca, Sakarya University, Faculty of Engineering, Department of Civil Engineering, Thesis, Sakarya, Turkey.
23. Haykin, S., (1998), Neural Networks-A Comprehensive Foundation (second ed.), 26-32. Prentice-Hall, Upper Saddle River, New Jersey, USA.
24. Kisi, O., Yuksel, I., and Dogan, E., (2008), Modelling daily suspended sediment of rivers in Turkey using several data driven techniques, Hydrological Sciences Journal, 53, 6, 1270-1285.
25. Hagan, M.T. and Menhaj, M.B., (1994), Training feed forward networks with the marquardt algorithm, IEEE Trans, Neural Networks, 6, 861-867.
26. Cybenco, G., (1989), Approximation by superposition of a sigmoidal function. Mathematics of Control, Signals and Systems 2, 303-314.
27. Hornik, K., Stinchcombe, M. and White, H. (1989), Multilayer Feed Forward Networks are Universal Approximators, Neural Networks 2,359-366. (Pubitemid 20609008)
28. Freiwan, M and Cigizoglu, H.K., (2005), Prediction of total monthly rainfall in Jordan using feed forward backpropagation method, Fresenius Environmental Bulletin, 14, 142-151.
29. Karakitsios, S.P., Hadjidakis, J., Kassomenos, P.A. and Pilidis, G.A., (2006), Development of an Artificial Neural Network to Predict Benzene Concentrations in A Street Canyon, Fresenius Environmental Bulletin, 15,424-430. (Pubitemid 43799157)
30. Diamantopoulou, M.J., Georgiou, P.E. and Papamichail, D.M., (2007) Performance of neural network models with kaiman learning rule for flow routing in a river system, Fresenius Environmental Bulletin, 16, 1474-1484.