Scholarly record
FORECASTING THE RAINFALL DATA BY ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM
Abstract
Konya is the biggest city of Turkey in terms of area and agricultural land, on the other hand sixth biggest city in terms of population. Because of the decrease of rainfall and increase in temperature, the agricultural production and daily water consumption are effected negatively in last years. Rainfall, one of the basic parameters of the hydrological cycle, has a big importance to determine the water budgets and to improve the water supply policy. In this study, monthly total rainfall data belong to Konya between 1970-2002 years, have been studied to forecast by Adaptive Neuro-Fuzzy Inference System (ANFIS). And model’s performance has been evaluated by comparison with the Lineer Regression (LR) as one of the traditional methods.
Publication details
References14
Chang, F.J., Chen, Y.C., 2001. A counterpropagation fuzzy-neural network modeling approach to real time stream flow prediction. J Hydrol.245, 153-64.
Chang, L.C., Chang, F.J., Chiang, Y.M., 2004. A two-step-ahead recurrent neural network for stream-flow forecasting. Hydrol Process. 18(1), 81-92.
Dawson, C.W., Wilby, R.L., 1998. An artificial neural network approach to rainfallrunoff modeling. Hydrol Sci. 43 (1), 47-67.
Drake, J.T., 2000. Communications phase synchronization using the adaptive network fuzzy inference system. Ph.D. Thesis, New Mexico State University, Las Cruces, New Mexico, USA.
Grimes, D.I.F., Coppola, E., Verdecchia, M., Visconti, G., 2003. A neural network approach to real-time rainfall estimation for Africa using satellite data. J Hydrometeorol. 4, 1 1 19-33.
Hasebe, M., Nagayama, Y., 2002. Reservoir operation using the neural network and fuzzy systems for dam control and operation support. Adv Eng Software. 33 (5), 24560.
Hsu, K.L., Gupta, H.V., Sorooshian, S., 1995. Artificial neural network modeling of the rainfall-runoff process. Water Resour Res. 31(10), 2517-30.
Jang, J.-S.R., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst Manag. Cyber. 23 (3), 665-685.
Jang, J.-S.R., Sun, C.-T, Mizutani, E., 1997. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall, Upper Saddle River, NJ. [II] Karunanithi, N., Grenney, W.J., Whitley, D., 1994. Neural network for river flow prediction. J Comput Civil Eng. 8, 201-20.
Lallahem, S., Mania, J., 2003. Evaluation and forecasting of daily groundwater outflow in a small chalky watershed. Hydrol Process. 17 (8), 1561-77.
Luk, K.C., Ball, JE., Sharma, ?., 2000. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J Hydrol. 227, 56-65.
Xu, Z.X., Li, J.Y., 2002. Short-term inflow forecasting using an artificial neural network model. Hydrol Process. 16, 2433-9.
Yang, C.C., Prasher, S.O., Lacroix, R., Sreekanth, S., Paini, N.K., Masse, L., 1997. Artificial neural network model for subsurfacedrained farmland. J Irrigat Drainage Eng. 123,285-92.
Yarar, ?., Onucyildiz, M., Copty, N.K., 2009. Modelling level changes in lakes using neuro-fuzzy and artificial neural networks. J. Hydrol. 365, 329-334.
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.
