Prediction of Flow Through Rockfill Dams Using a Neuro-fuzzy Computing Technique

Volume 2, Issue 3, pp 515--528 http://dx.doi.org/10.22436/jmcs.02.03.15

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Authors

Majid Heydari - Department of Irrigation, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran Parisa Hosseinzadeh Talaee - Department of Irrigation, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

Abstract

Rockfill dams are economical and fast tools for flood detention and control purposes. Artificial intelligence approaches may provide user-friendly alternatives to very complex and time-consuming numerical methods such as finite volume and finite element for predicting flow through rockfill dam. Therefore, this paper examines the potential of coactive neuro-fuzzy inference system (CANFIS) for estimation of flow through trapezoidal and rectangular rockfill dams. The results showed that accurate flow predictions can be achieved with a CANFIS with the Takagi–Sugeno–Kang (TSK) fuzzy model and the Bell membership function for both trapezoidal and rectangular rockfill dams. Furthermore, Levenberg-Marquardt and Delta-Bar-Delta were the best algorithms for training the network in order to estimate flow through rectangular and trapezoidal rockfill dams, respectively. Overall, the results of this study suggest the possibility for using CANFIS for prediction of flow through rockfill dam.

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Keywords

• Flow forecast
• Rockfill dam
• Coactive neuro-fuzzy inference system

MSC

•  76M99
•  68T05

References

• [1] S. Aghakhani, Neuro-fuzzy architecture based on complex fuzzy logic, Doctoral dissertation (University of Alberta), 2010 (2010), 134 pages
  • Google Scholar


• [2] E. A. Lim, Y. Jayakumar, A study of neuro-fuzzy system in approximation-based problems, Matematika, 24 (2008), 113--130
  • View Article
  • Google Scholar


• [3] A. Aytek, Co-active neurofuzzy inference system for evapotranspiration modeling, Soft Computing, 7 (2008), 691--700
  • View Article
  • Google Scholar


• [4] G. Camps-Valls, L. Gómez-Chova, J. Calpe-Maravilla, J. D. Martín-Guerrero, E. Soria-Olivas, L. Alonso-Chordá, J. Moreno, Robust support vector method for hyperspectral data classification and knowledge discovery, IEEE Transactions on Geoscience and Remote sensing, 42 (2004), 1530--1542
  • View Article
  • Google Scholar


• [5] F. J. Chang, Y. T. Chang, Adaptive neuro-fuzzy inference system for prediction of water level in reservoir, Advances in water Resources, 29 (2006), 1--10
  • View Article
  • Google Scholar


• [6] Y. T. Chang, L. C. Chang, F. J. Chang, Intelligent control of modeling of real time reservoir operation, Hydrological Processes, 9 (2001), 1621--1634
  • View Article
  • Google Scholar


• [7] Y. T. Chang, L. C. Chang, F. J. Chang, Intelligent control for modeling of real‐time reservoir operation, part II: artificial neural network with operating rule curves, Hydrological Processes, 19 (2005), 1431--1444
  • View Article
  • Google Scholar


• [8] M. Cobaner, B. Unal, O. Kisi, Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data, Journal of Hydrology, 367 (2009), 52--61
  • View Article
  • Google Scholar


• [9] P. Deka, V. Chandramouli, A fuzzy neural network model for deriving the river stage--discharge relationship, Hydrol. Sci. J., 2 (2003), 197--209
  • View Article
  • Google Scholar


• [10] B. Dixon, Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis, Journal of Hydrology, 309 (2005), 17--38
  • View Article
  • Google Scholar


• [11] F. Mohamed, A new approach for extracting fuzzy rules using artificial neural networks, M.Sc. Thesis Computer Science (Cairo University), 2005 (2005), 122 pages
  • Google Scholar


• [12] L. H. Feng, Z. R. Wu, Application of R/S analysis to forecasting the variable trend of precipitation, Theory Method Appl. Syst. Eng., 1 (2001), 79--81
  • Google Scholar


• [13] M. Firat, M. Güngör, Hydrological time-series modelling using an adaptive neuro-fuzzy inference system, Hydrological Processes, 13 (2008), 2122--2132
  • View Article
  • Google Scholar


• [14] D. K. Gautam, K. P. Holz, Rainfall–runoff modelling using adaptive neuro-fuzzy systems, Journal of Hydroinformatics, 3 (2001), 3--10
  • View Article
  • Google Scholar


• [15] S. Gupta, A. Javed, D. Datt , Economics of Flood Protection in India, Flood Problem and Management in South Asia, 28 (2003), 199--210
  • View Article
  • Google Scholar


• [16] J. W. Hall, P. B. Sayers, R. J. Dawson, National-scale assessment of current and future flood risk in England and Wales, Natural Hazards, 36 (2005), 147--164
  • View Article
  • Google Scholar


• [17] F. Hardalaç, A. T. Ozan, N. Barişçi, U. Ergün, S. Serhatlioglu, I. Guler, The Examination of the Effects of Obesity on a Number of Arteries and Body Mass Index by Using Expert Systems, Journal of Medical Systems, Vol. 28, 129--142 (2004)
  • View Article
  • Google Scholar


• [18] Y. S. T. Hong, P. A. White, Hydrological modeling using a dynamic neuro-fuzzy system with on-line and local learning algorithm, Advances in Water Resources, 32 (2009), 110--119
  • View Article
  • Google Scholar


• [19] F. Imamura, D. Van To, Flood and typhoon disasters in Viet Nam in the half century since 1950, Natural Hazards, 15 (1997), 71--87
  • View Article
  • Google Scholar


• [20] A. P. Jacquin, A. Y. Shamseldin, Development of rainfall–runoff models using Takagi–Sugeno fuzzy inference systems, Journal of Hydrology, 329 (2006), 154--173
  • View Article
  • Google Scholar


• [21] J. S. R. Jang, C. T. Sun, E. Mizutani, Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice-Hall , New Jersey (1997)
  • View Article
  • Google Scholar


• [22] J. L. Jin, J. Cheng, Y. M. Wei, Forecasting flood disasters using an accelerated genetic algorithm: Examples of two case studies for China, Natural hazards, 44 (2008), 85--92
  • View Article
  • Google Scholar


• [23] O. Kisi, Daily pan evaporation modelling using a neuro- fuzzy computing technique, Journal of hydrology, 329 (2006), 636--646
  • View Article
  • Google Scholar


• [24] O. Kisi, Suspended sediment estimation using neuro-fuzzy and neural network approaches, Hydrological Sciences Journal, 4 (2005), 683--696
  • View Article
  • Google Scholar


• [25] Ö. Kişi, Ö. Öztürk, Adaptive neurofuzzy computing technique for evapotranspiration estimation, J. Irrig. Drain. Eng., 4 (2007), 368--379
  • View Article
  • Google Scholar


• [26] O. Kisi, T. Haktanir, M. Ardiclioglu, O. Ozturk, ٍE. Yalcin, S. Uludag, Adaptive neuro-fuzzy computing technique for suspended sediment estimation, Advances in Engineering Software, 40 (2009), 438--444
  • View Article
  • Google Scholar


• [27] B. Kurtulus, M. Razack, Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy, Journal of Hydrology, 381 (2010), 101--111
  • View Article
  • Google Scholar


• [28] A. K. Lohani, N. K. Goel, K. K. S. Bhatia, Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship, Journal of Hydrology, 331 (2006), 146--160
  • View Article
  • Google Scholar


• [29] R. S. Lu, S. L. Lo, Diagnosing reservoir water quality using self organising maps and fuzzy theory, Water Resources, 9 (2002), 2265--2274
  • View Article
  • Google Scholar


• [30] A. Moghaddamnia, M. G. Gousheh, J. Piri, S. Amin, D. Han, Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques, Advances in Water Resources, 32 (2009), 88--97
  • View Article
  • Google Scholar


• [31] D. Nauck, R. Kruse, A neuro-fuzzy method to learn fuzzy classification rules from data, Fuzzy Sets and Systems, 89 (1997), 277--288
  • View Article
  • Google Scholar


• [32] P. C. Nayak, K. P. Sudheer, D. M. Rangan, K. S. Ramasastri, A neuro-fuzzy computing technique for modeling hydrological time series, Journal of Hydrology, 291 (2004), 52–66
  • View Article
  • Google Scholar


• [33] P. C. Nayak, K. P. Sudheer, D. M. Rangan, K. S. Ramasastri, Short-term flood forecasting with a neuro-fuzzy model, Water Resources Research, Vol .41, 16 pages (2005)
  • View Article
  • Google Scholar


• [34] B. Omidvar, H. Khodaei, Using value engineering to optimize flood forecasting and flood warning systems: Golestan and Golabdare watersheds in Iran as case studies, Natural hazards, 47 (2008), 281--296
  • View Article
  • Google Scholar


• [35] L. Parthiban, R. Subramanian, Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm, International Journal of Biological and Life Sciences, Vol. 3 (2008)
  • View Article
  • Google Scholar


• [36] K. Ponnambalam, F. Karray, S. J. Mousavi, Minimizing variance of reservoir systems operations benefits using soft computing tools, Fuzzy Sets and Systems, 139 (2003), 451--461
  • View Article
  • Google Scholar


• [37] T. Rajaee, S. A. Mirbagheri, M. Zounemat-Kermani, V. Nourani, Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models, Science of the Total Environment, 407 (2009), 4916--4927
  • View Article
  • Google Scholar


• [38] J. M. V. Samani, M. Heydari, Reservoir Routing through successive Rockfill detention Dams, J. Agric. Sci. Technol., 9 (2007), 317--326
  • View Article
  • Google Scholar


• [39] H. M. V. Samani, J. M. V. Samani, M. Shayannejad, Reservoir routing using steady and unsteady flow through rockfill dam, Journal of Hydraulic Engineering, 6 (2003), 448--454
  • View Article
  • Google Scholar


• [40] G. Setlak, The fuzzy-neuro classifier for decision support, International Journal Information Theories & Applications, Vol.15 (2008)
  • View Article
  • Google Scholar


• [41] T. N. Singh, A. K. Verma, P. K. Sharma, A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis, Geotechnical and Geological Engineering, 25 (2007), 395--407
  • View Article
  • Google Scholar


• [42] C. Slim, Neuro-Fuzzy Network based on Extended Kalman Filtering Science for Financial Time Series, World Academy of Engineering and Technology, Vol. 22, 134--139 (2006)
  • View Article
  • Google Scholar


• [43] D. J. Stephenson, Rockfill in Hydraulic Engineering, Elsevier Scientific Publishing Company, New York (1979)
  • View Article
  • Google Scholar


• [44] A. Talei, L. H. C. Chua, C. Quek, A novel application of a neuro-fuzzy computational technique in event-based rainfall–runoff modeling, Expert Syst. Appl., 37 (2010), 7456--7468
  • View Article
  • Google Scholar


• [45] W. H. Teng, M. H. Hsu, C. H. Wu, A. S. Chen, Impact of flood disasters on Taiwan in the last quarter century, Natural Hazards, 37 (2006), 191--207
  • View Article
  • Google Scholar


• [46] A. Tortum, N. Yayla, M. Gökdağ, The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system, Expert Syst. Appl., 36 (2009), 6199--6217
  • View Article
  • Google Scholar


• [47] Y. M. Wei, J. L. Jin, The research advances of flood disaster research, Nat Explore, 2 (1998), 6--10
  • Google Scholar


• [48] Y. Yu, B. Zhang, H. Yuan, An intelligent displacement back-analysis method for earth-rockfill dams, Computers and Geotechnics, 34 (2007), 423--434
  • View Article
  • Google Scholar


• [49] M. Zounemat-Kermani, A. A. Beheshti, B. Ataie-Ashtiani, S. R. Sabbagh-Yazdi, Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system, Appl. Soft Comput., 9 (2009), 746--55
  • View Article
  • Google Scholar