Prediction of Darbandikhan Reservoir Inflow Using ANFIS Models.

Hekmat M. Ibrahim

Faculty of Engineering-Sulaimani University, Bakrajo Street, Sulaimaniyah-lraq

DOI: https://doi.org/10.17656/jzs.10618

Original: 1 October 2016, Revised: 23 December 2016, Accepted: 9 January 2017, Published online: 20 June 2017

Abstract

Predicting reservoir inflow is important to reservoir management and scheduling; therefore, accurate prediction of reservoir inflow has been a vital task for researchers and water resources managers during last decades. Recently, Adaptive Neuro-Fuzzy Inference System (ANFIS) has been extensively used to find the relationship between inputs and outputs without considering the physical process of the phenomena. Therefore, the current study investigates and evaluates the capability and applicability of the ANFIS technique in modeling the reservoir inflow for predicting future values of monthly inflow to Darbandikhan reservoir in Kurdistan Region, Iraq. Five rain gage stations data from 1992-2009 were used to develop ANFIS models to predict monthly reservoir inflow from various combinations of antecedent values of inflow and rainfall within the basin in Iraq and Iran which are considered as input variables. Two different membership functions (MFs), triangular and generalized bell, with different numbers (2-5) for each input variable were investigated in the ANFIS models. The best fit models were selected using three performance evaluation criteria, namely; coefficient of determination ( R 2), coefficient of efficiency ( CE ) and normalized root mean squared error (  NRMSE  ). In comparison of ANFIS model uses the triangular MF with that model uses the generalized bell MF, approximately, similar predict accuracies were obtained for both MFs except the latter needs fewer numbers of MFs. The results indicated that using a combination of inflow and rainfall as input variables is effective in improving predict accuracy and developing parsimonious models with fewer and readily available input variables. Moreover, among different architectures of ANFIS, structures, including three input variables, 1 and 12 time lags of inflow ( It-1 , It-12  ) and rainfall of Marivan station ( ) showed better performance for this application. For best models, the performance evaluation criteria, , and , for checking data set were obtained as 0.96, 0.95 and 0.2 respectively for model with four triangular MFs; and as 0.96, 0.96 and 0.2 respectively for model with three generalized bell MFs. Reservoir inflow modeling in this way will be more reliable than doing it using a time series model as a more effective parameter could be incorporated. By considering the results, ANFIS method is an effective tool that can be successfully applied for reservoir inflow modeling and have satisfactory performances in Darbandikhan reservoir monthly inflow prediction.

Key Words:

Darbandikhan

Reservoir

Inflow

Prediction

ANFIS

References

[1] A. Shalamu, Ph.D Thesis, "Las Cruces, New Mexico: New Mexico State University, 2009.

[2] C.-T. Cheng, Z.-K. Feng, W.-J. Niu and S.-L. Liao, "Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China", Water, Vol. 7, pp. 4477-4495, (2015).

[3] H. Galavi and L. T. Shui, "Neuro-Fuzzy Modelling and Forecasting in Water Resources", Scientific Research and Essays, Vol. 7, No. 24, pp. 2112-2121, (2012).

[4] A. Altunkaynak, M. Ozger and M. Cakmakci, "Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach", Water Resources Management, Vol. 19, No. 5, pp. 641-654, (2005).

[5] M. Firat, M. E. Turan and M. A. Yurdusev, "Comparative analysis of Fuzzy Inference Systems for Water Consumption Prediction", Hydrology, Vol. 374, No. 3, pp. 235-241, (2009).

[6] M. T. Dastorani, H. Afkhami, H. Sharifidarani and M. Dastorani, "Application of ANN and ANFIS Models on Dryland Precipitation Prediction (Case Study: Yazd in Central Iran)", Applied Sciences, Vol. 10, No. 20, pp. 2387-2394, (2010).

[7] J.-Y. Lin, C.-T. Cheng, Y.-G. Sun and K. Chau, "Long-Term Prediction of Discharges in Manwan Hydropower Using Adaptive-Network-Based Fuzzy Inference Systems Models", Lecture Notes in Computer Science, Vol. 3612, pp. 1152-1161, (2005).

[8] P. C. Nayak, K. P. Sudheer, D. M. Rangan and K. S. Ramasastri, "Short-term Flood Forecasting With a Neurofuzzy Model", Water Resources Research, Vol. 41, No. W04004, pp. 1-16, (2005).

[9] K. Shahverdi, K. Khodaei and A. Shokohi, "Prediction of Monthly Inflow to Dam Reservoir Using ANN and ANFIS: A Case Study of Latyan Dam", International Bulletin of Water Resources & Development, vol. III, no. 03, pp. X-XVIII, (2015).

[10] "Darbandikhan Basin Project", Nature Iraq/Twin Rivers Environmental Research Institute/The American University of Iraq-Sulaimani, Sulaimani, Kurdistan, Iraq, (2008).

[11] "Darbandikhan Lake Poisoning Event", Nature Iraq/Twin Rivers Environmental Research Institute/The American University of Iraq-Sulaimani, Sulaimani, Kurdistan, Iraq, (2008).

[12] M. C. Cordell, "Consultancy Services for Dokan and Derbendikhan Dam Inspections", The World Bank/SMEC International Pty. Ltd., Melbourne, Australia, (2006).

[13] "Islamic Republic of Iran Meteorological Organization", Chaharmahal va Bakhtiari Meteorological Administration, [Online]. Available: http://www.chaharmahalmet.ir/iranarchive.asp. [Accessed 21 8 2016].

[14] S. Makridakis, S. C. Wheelwright and R. J. Hyndman, "Forecasting, Methods and Applications", 3rd ed., New York: John Wiley and Sons, Inc., (1998).

[15] A. George and U. C. Ionaa, "Forecasting Tourism Demand Using ANFIS for Assuaring Successful Strategies in the View of Sustainable Development in the Tourism Sector", in 2nd IASME / WSEAS International Conference on Energy & Environment, Portoroz, Slovenia, (2007).

[16] A. K. Palit and D. Popovic, "Computational Intelligence in Time Series Forecasting, Theory and Engineering Applications", London: Springer-Verlag London Limited, (2005).

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