Monthly River Flow Prediction using Adaptive Neuro-Fuzzy Inference System (A Case Study: Gharasu Watershed, Ardabil Province-Iran)

Authors
H. Akbari 1
M. Vafakhah 2
1 PhD Student, Department of Watershed Management, Faculty of Natural Resources Environment, Tarbiat Modares University, Mazandaran, Nour, Iran
2 Associate Professor, Department of Watershed Management, Faculty of Natural Resources Environment, TarbiatModares University, Mazandaran, Noor, Iran
Abstract
There is different methods for simulating river flow. Some of thesemethods such as the process based hydrological models need multiple input data and high expertise about the hydrologic process. But some of the methods such as the regression based and artificial inteligens modelsare applicable even in data scarce conditions. This capability can improve efficiency of the hydrologic modeling in ungauged watersheds in developing countries. This study attempted to investigate the capability of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for simulating the monthly river flow in three hydrometric stations of Pole-Almas, Nir, and Lai; which have different rate of river flow. The simulations are conducted using three input data including the precipitation, temperature, and the average monthly hydrograph (AMH). The study area islocated in the Gharasu Watershed, Ardabil Province, Iran. For this aim, six groupsof input data (M1, M2, … M6) were defined based on different combinations of the above-mentioned input data. Theconducted simulations in Pole-Almas and Nir stations have presented an acceptable results; but in Lai station it was very poor. This different behavoirs was referred to the lower volume of flow and consequently irregularity and variability of flow in Lai station, which cause the decrease of accuracy in the simulation. The AMH parameter had an important role in increasing the accuracy of the simulations in Pole-Almas and Nir stations. The findings of this study showed that ANFIS is an efficient tool for river flow simulation; but in application of ANFIS, the selection and utilization of relevant and efficient input data will have a determinativerole in achieving to a successful modeling.
Keywords
artificial neural network
Average monthly hydrograph
Fuzzy Logic
Rainfall-runoff modeling
Akbari Majdar, H., Bahremand, A.R., Najafinejad, A. and Sheikh, V.B. Daily flow simulation of Chehelchai river-Golestan province using SWAT model, J. Water Soil Conserv., 2013; 20(3): 253-259. (In Persion)
Aqil, M., Kita, I., Yano, A. and Nishiyama, S. A comparative study of artificial neural networks and neuro-fuzzy in continuous modelling of the daily and hourly behaviour of runoff. J. Hydrol., 2007; 337(2): 22-34.
Arnold, J.G., Srinivasan, R., Muttiah, R.S. and Williams, J.R. Large  area hydrological modelling and assessment Part I: M development. J. Am. Water Resour. Assoc., 1998; 34(1): 73-89.
Bosch, D.D., Bingner, R.L. Theurer, F.D. Felton, G. and Chaubey, I. Evaluation of the AnnAGNPS water quality model. presented at the ASAE annual international meeting. Orlando, florida. 1998. Paper no: 982195.
Bouraoui, F. and Dillaha, T. ANSWERS-2000: runoff and sediment transport model. J. Environ. Eng.,1996; 122(6): 493-502.
Chang, F.J., Hu, H.F. and Chen, Y.C.Counter propagation fuzzy–neural network for river flow reconstruction. Hydrol. Process., 2001; 15(2): 219-232.
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. Hydrol. Process., 2006; 20(7): 1525-1540.
Elabd, S. and Schlenkhoff, A.ANFIS and BP neural network for travel time prediction,World Academy of Science, Eng. Tech., 2009; 57: 116-121.
Firat, M. Artificial intelligence techniques for river flow forecasting in the seyhan river catchment-turkey. Hydrol. Earth Syst. Sci., 2007; 4(3): 1369-1406.
Green, I.R.A. and Stephenson, D. Criteria for comparison of single event models, Hydr. Sci. J., 1986; 31(3): 395-411.
He, Z., Wen, X., Liu, H. and Du, J.A.Comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region, J. Hydrol., 2014; 509: 379-386.
Hosseini S.M. and Mahjouri N. Integrating  support  vector  regression  and  a  geomorphologic  artificial neural  network  for  daily  rainfall-runoff  modeling, App.  Sof.  Comp., 2016; 38: 329-345.
Hsu, N.S., Huang, C.L. and Wei, C.C. Multi-phase intelligent decision model for reservoir real-time flood control during typhoonsOriginal Research Article, J. Hydrol., 2015; 522: 11-34.
Jang, J.S.R.,ANFIS: Adaptive-network-based fuzzy inference system. IEEET ransactions on Systems. Man and Cyb., 1993; 23(3): 665–685.
Jothiprakash, V. and Garg, V. Reservoir sedimentation estimation using artificial neural network. J. Hydrol. Eng., 2009; 14(9): 1035-1040.
Kadhim, H.H. Self learning of ANFIS inverse control using iterative learning technique, Int. J. Comp. App., 2011; 21(8): 24-29.
Keskin, M.E., Taylan, D. and Terzi, O.Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series. Hydrolog. Sci. J., 2006; 51(4): 588-598.
Kisi, O. River flow modeling using artificial neural networks. J. Hydrol. Eng., 2003; 9(1): 60-63.
Kumar A.R., Sudheer K.P., Jain S.K. and Agarwal P.K. Rainfall-runoff modelling using artificial neural networks: comparison of network types. Hydrol. Process., 2005; 19: 1277-1291.
Mutlu, E., Chaubey, I., Hexmoor, H. and Bajwa, S.G.Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrol. Process., 2008; 22(26): 5097-5106.
Nayak, P.C., Sudheer, K.P., Rangan D.M. and  Ramasastri K.S., A neuro-fuzzy computing technique for modeling hydrological time series. J. Hydrol., 2004; 291(1): 52-66.
Poff, N.L. and Ward, J.V. Implications of streamflow variability and predictability for lotic community structure: a regional analysis of streamflow patterns. Canadian J. Fish Aqu. Sci., 1989: 46(10); 1805-1818.
Shu, C. and Ouarda, T.B. Regional flood frequency nalysis at ungauged sites using the adaptive neuro-fuzzy inference system. J. Hydrol., 2008; 349(1): 31-43.
Takagi,  T.  and  Sugeno,  M. Fuzzy  identification systems  and  its  application  to  modelling  and  control. IEEE Transactions on Systems, Man, and Cybernetics., 1985; 15: 116-132.
Talei, A., Chua, L. H., Quek, C. and Jansson P. E. Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning. J. Hydrol., 2013; 488: 17-32.
Vafakhah, M. Application of artificial neural networks and adaptive neuro-fuzzy inference system models to short-term streamflow forecasting. Can. J. Civil. Eng., 2012; 39(4): 402-414.
Vafakhah, M., Janizadeh, S. and Khosrobeigi Bozchaloei, S. Application of Several Data-Driven Techniques for Rainfall-Runoff Modeling. ECOPERSIA, 2014; 2(1): 455-469.
Wang, W., Chau, K.W., Chang, C.T. and Qui, L.A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J. Hydrol., 2009; 374 (3-4): 294-306.
ECOPERSIA
Volume 3, Issue 4 - Serial Number 14
December 2015
Pages 1175-1188