Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: An application to Izmir, Turkey

Journal of Hydrology

Volumn 365, Issue 3-4, 2009, Pages 225-234

  Yurdusev, Mehmet Ali ^a   Firat, Mahmut ^b  

^a CELAL BAYAR UNIVERSITY   (Turkey)

^b PAMUKKALE UNIVERSITY   (Turkey)

Author keywords

ANFIS; ANN; Fuzzy logic; Prediction; Water consumption; Water demand

Indexed keywords

BACKPROPAGATION; ECONOMICS; FUZZY INFERENCE; FUZZY LOGIC; FUZZY SETS; POPULATION STATISTICS; WATER SUPPLY;

ANFIS; ANN; PREDICTION; WATER CONSUMPTION; WATER DEMAND;

FUZZY SYSTEMS;

ARTIFICIAL NEURAL NETWORK; CLIMATE EFFECT; FORECASTING METHOD; FUZZY MATHEMATICS; HYDROLOGICAL MODELING; WATER DEMAND; WATER USE;

EURASIA; IZMIR [TURKEY]; TURKEY;

EID: 58849164849     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2008.11.036     Document Type: Article

References (33)

1. Agarwal A., Mishra S.K., Ram S., and Singh J.K. Simulation of runoff and sediment yield using artificial neural networks. Biosystems Engineering 94 4 (2006) 597-613
2. Altunkaynak A., Özger M., and Çakmakci{dotless} M. Water consumption prediction of Istanbul city by using fuzzy logic approach. Water Resources Management 19 (2005) 641-654
3. ASCE Task Committee. Artificial neural networks in hydrology-II: hydrologic applications. Journal of Hydrologic Engineering ASCE 5 2 (2000) 124-137
4. Babel M.S., Gupta A.D., and Pradhan P. A multivariate econometric approach for domestic water demand modeling: an application to Kathmandu, Nepal. Water Resources Management 21 (2007) 573-589
5. Bougadis J., Adamowski K., and Diduch R. Short-term municipal water demand forecasting. Hydrological Processes 19 (2005) 137-148
6. Chang F.J., and Chang Y.T. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources 29 (2006) 1-10
7. Chen S.H., Lin Y.H., Chang L.C., and Chang F.J. The strategy of building a flood forecast model by neuro fuzzy network. Hydrological Processes 20 (2005) 1525-1540
8. Firat, M., 2007. Watershed modeling by adaptive neuro-fuzzy inference system approach. Doctor of Philosophy Thesis, Pamukkale University, Turkey, (in Turkish).
9. Firat M., and Güngör M. River flow estimation using adaptive neuro-fuzzy inference system. Mathematics and Computers in Simulation 75 3-4 (2007) 87-96
10. Firat M., and Güngör M. Hydrological time-series modeling using adaptive neuro- fuzzy inference system. Hydrological Processes 22 13 (2008) 2122-2132
11. Froukh M.L. Decision-support system for domestic water demand forecasting and management. Water Resources Management 15 (2001) 363-382
12. Jain A., and Kumar A.M. Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing 7 (2007) 585-592
13. Jain A., Varshney A.K., and Joshi U.C. Short-term water demand forecast modeling at IIT Kanpur using artificial neural networks. Water Resources Management 15 (2001) 299-321
14. Jang J.S.R., Sun C.T., and Mizutani E. Neuro-Fuzzy and Soft Computing (1997), PrenticeHall. 0-13-261066-3 (607p.)
15. Jeong D., and Kim Y.O. Rainfall-runoff models using artificial neural networks for ensemble stream flow prediction. Hydrological Processes 19 (2005) 3819-3835
16. Komornik J., Komornikova M., Mesiar R., Szökeova D., and Szolgay J. Comparison of forecasting performance of nonlinear models of hydrological time series. Physics and Chemistry of the Earth 31 (2006) 1127-1145
17. Kumar A.R.S., Sudheer K.P., Jain S.K., and Agarwal P.K. Rainfall-runoff modelling using artificial neural networks: comparison of network types. Hydrological Processes 19 (2005) 1277-1291
18. Lahlou M., and Colyer D. Water conservation in Casablanca, Morocco. Journal of American Water Resource Association 36 5 (2000) 1003-1012
19. Liong S.Y., Lim W.H., Kojiri T., and Hori T. Advance flood forecasting for flood stricken Bangladesh with a fuzzy reasoning method. Hydrological Processes 14 (2000) 431-448
20. Liu J., Savenije H.H.G., and Xu J. Forecast of water demand in Weinan City in China using WDF-ANN model. Physics and Chemistry of the Earth 28 (2003) 219-224
21. Mahabir C., Hicks F.E., and Fayek A.R. Application of fuzzy logic to the seasonal runoff. Hydrological Processes 17 (2003) 3749-3762
22. Mitra B., Scott H.D., Dixon J.C., and Mckimmey J.M. Application of fuzzy logic to the prediction of soil erosion in a large watershed. Geoderma 86 (1998) 183-209
23. Nagy H.M., Watanabe K., and Hirano M. Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulic Engineering 128 (2002) 588-595
24. Nayak P.C., Sudheer K.P., Rangan D.M., and Ramasastri K.S. A neuro fuzzy computing technique for modeling hydrological time series. Journal of Hydrology 291 (2004) 52-66
25. Nayak P.C., Sudheer K.P., and Ramasastri K.S. Fuzzy computing based rainfall-runoff model for real time flood forecasting. Hydrological Processes 19 (2005) 955-968
26. Rajurkar M.P., Kothyari U.C., and Chaube U.C. Modeling of the daily rainfall-runoff relationship with artificial neural network. Journal of Hydrology 285 (2004) 96-113
27. Sajikumar N., and Thandaveswara B.S. A non-linear rainfall-runoff model using an artificial neural network. Journal of Hydrology 216 (1999) 32-55
28. Şen Z. Fuzzy Logic and Foundation (2001), BKS Publisher. 9758509233 (in Turkish, 172 p.)
29. Şen Z., and Altunkaynak A. A comparative fuzzy logic approach to runoff coefficient and runoff estimation. Hydrological Processes 20 (2006) 1993-2009
30. Wong L.T., and Mui K.W. Modeling water consumption and flow rates for flushing water systems in high-rise residential buildings in Hong Kong. Building and Environment 42 (2007) 2024-2034
31. Zadeh L.A. Fuzzy sets. Information and Control 8 3 (1965) 338-353
32. Zhou S.L., McMahon T.A., Walton A., and Lewis J. Forecasting daily urban water demand: a case study of Melbourne. Journal of Hydrology 236 3 (2000) 153-164
33. Zhou S.L., McMahon T.A., and Wang O.J. Frequency analysis of water consumption fore metropolitan of Melbourne. Journal of Hydrology 247 (2001) 72-84