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Accurately forecasted reservoir water level is among the most vital data for efficient reservoir structure design and management. In this study, the group method of data handling is combined with the minimum description length method to develop a very practical and functional model for predicting reservoir water levels. The models’ performance is evaluated using two groups of input combinations based on recent days and recent weeks. Four different input combinations are considered in total. The data collected from Chahnimeh#1 Reservoir in eastern Iran are used for model training and validation. To assess the models’ applicability in practical situations, the models are made to predict a non-observed dataset for the nearby Chahnimeh#4 Reservoir. According to the results, input combinations (L, L-1) and (L, L-1, L-12) for recent days with root-mean-squared error (RMSE) of 0.3478 and 0.3767, respectively, outperform input combinations (L, L-7) and (L, L-7, L-14) for recent weeks with RMSE of 0.3866 and 0.4378, respectively, with the dataset from https://www. typingclub.com/st. Accordingly, (L, L-1) is selected as the best input combination for making 7-day ahead predictions of reservoir water levels.
Wydawca
Czasopismo
Rocznik
Tom
Strony
717--730
Opis fizyczny
Bibliogr. 46 poz.
Twórcy
autor
- Department of Civil Engineering, Razi University, Kermanshah, Iran
autor
- Department of Civil Engineering, Razi University, Kermanshah, Iran
autor
- School of Engineering, University of Guelph, Guelph, ON NIG 2W1, Canada
Bibliografia
- 1. Altunkaynak A (2007) Forecasting surface water level fluctuations of lake van by artificial neural networks. Water Resour Manag 21(2):399–408
- 2. Barzegar R, Moghaddam AA, Adamowski J, Ozga-Zielinski B (2018) Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model. Stoch Environ Res Risk Assess 32(3):799–813
- 3. Crapper PF, Fleming PM, Kalma JD (1996) Prediction of lake levels using water balance models. Environ Softw 11(4):251–258
- 4. Ebtehaj I, Bonakdari H, Khoshbin F, Azimi H (2015) Pareto genetic design of group method of data handling type neural network for prediction discharge coefficient in rectangular side orifices. Flow Meas Instrum 41:67–74
- 5. Ebtehaj I, Bonakdari H, Khoshbin F (2016) Evolutionary design of a generalized polynomial neural network for modeling sediment transport in clean pipes. Eng Optim 48(10):1793–1810
- 6. Ebtehaj I, Bonakdari H, Gharabaghi B (2018) Development of more accurate discharge coefficient prediction equations for rectangular side weirs using adaptive neuro-fuzzy inference system and generalized group method of data handling. Measurement 116:473–482
- 7. Gholami A, Bonakdari H, Ebtehaj I, Shaghaghi S, Khoshbin F (2017) Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed. Earth Surf Process 42(10):1460–1471
- 8. Gholami A, Bonakdari H, Ebtehaj I et al (2018) A methodological approach of predicting threshold channel bank profile by multi-objective evolutionary optimization of ANFIS. Eng Geol 239:298–309
- 9. Gladkov EG, Eletskii VS, Zhabin VF (1991) Prediction of the change in the water level of Lake Sarez and characteristics of seepage through the Usoi barrier. Plenum Publishing Corporation, New York
- 10. Grünwald PD, Myung IJ, Pitt MA (2005) Advances in minimum description length: theory and applications. MIT Press, Massachusetts
- 11. Guganesharajah K, Shaw EM (1984) Forecasting water levels for Lake Chad. Water Resour Res 20(8):1053–1065
- 12. Güldal V, Tongal H (2010) Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Eğirdir lake level forecasting. Water Resour Manag 24(1):105–128
- 13. Haykin S, Network N (2004) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River
- 14. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
- 15. Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern 42(2):513–529
- 16. Iba H, Sato T, de Garis H (1994) System identification approach to genetic programming. In: IEEE world congress on computational intelligence, Orlando, Florida, USA
- 17. Ivakhnenko A (1970) Heuristic self-organization in problems of engineering cybernetics. Automatica 6(2):207–219
- 18. Ivakhnenko A (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern SMC-1(4):364–378
- 19. Kakahaji H, Banadaki HD, Kakahaji A, Kakahaji A (2013) Prediction of Urmia Lake water-level fluctuations by using analytical, linear statistic and intelligent methods. Water Resour Manag 27(13):4469–4492
- 20. Khan MS, Coulibaly P (2006) Application of support vector machine in lake water level prediction. J Hydrol Eng 11(3):199–205
- 21. Kisi O, Shiri J, Nikoofar B (2012) Forecasting daily lake levels using artificial intelligence approaches. Comput Geosci 41:169–180
- 22. Kisi O, Shiri J, Karimi S et al (2015) A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm. Appl Math Comput 270:731–743
- 23. Koppula SD (1980) Forecasting lake levels—a case study. In: National symposium on urban stormwater management in coastal areas, Va Tech, ASCE, New York, USA
- 24. Lan Y (2014) Forecasting performance of support vector machine for the Poyang Lake’s water level. Water Sci Technol 70(9):1488–1495
- 25. Liu H, Sun S, Zheng T, Li G (2018) Prediction of water temperature regulation for spawning sites at downstream of hydropower station by artificial neural network method. Trans Chin Soc Agric Eng 34(4):185–191
- 26. Mahdi Hadi R, Shokri S, Ayubi P (2013) Urmia Lake level forecasting using Brain Emotional Learning (BEL). In: 3rd International conference on computer and knowledge engineering, ICCKE 2013, Mashhad, Iran
- 27. Najafzadeh M, Azamathulla HM (2013) Group method of data handling to predict scour depth around bridge piers. Neural Comput Appl 23(7–8):2107–2112
- 28. Najafzadeh M, Barani GA (2011) Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers. Sci Iran 18(6):1207–1213
- 29. Najafzadeh M, Bonakdari H (2017) Application of neuro-fuzzy GMDH model for predicting the velocity at limit of deposition in storm sewers without deposited beds and under non-cohesive bed load sediment transport conditions. J Pipeline Syst Eng 8(1):06016003-1:8
- 30. Najafzadeh M, Lim SY (2015) Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates. Earth Sci Inform 8(1):187–196
- 31. Najafzadeh M, Tafarojnoruz A (2016) Evaluation of neuro-fuzzy GMDH-based particle swarm optimization to predict longitudinal dispersion coefficient in rivers. Environ Earth Sci 75(2):157
- 32. Najafzadeh M, Barani GA, Azamathulla HM (2014) Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling. Neural Comput Appl 24(3–4):629–635
- 33. Nariman-zadeh N, Darvizeh A, Darvizeh M, Gharababaei H (2002) Modelling of explosive cutting process of plates using GMDH-type neural network and singular value decomposition. J Mater Process Technol 128(1–3):80–87
- 34. Nariman-zadeh N, Darvizeh A, Jamali A, Moeini A (2005) Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process. J Mater Process Technol 164–165:1561–1571
- 35. Neyshaburi MR, Bayat H, Mohammadi K, Nariman-zadeh N, Irannejad M (2015) Improvement in estimation of soil water retention using fractal parameters and multiobjective group method of data handling. Arch Agron Soil Sci 61:257–273
- 36. Nikolaev NI, Iba H (2001) Accelerated genetic programming of polynomials. Genet Program Evol Mach 2(3):231–257
- 37. Ondimu S, Murase H (2007) Reservoir level forecasting using neural networks: Lake Naivasha. Biosyst Eng 96(1):135–138
- 38. Poggio T, Girosi F (1990) Regularization algorithms for learning that are equivalent to multilayer networks. Science 247(4945):978–982
- 39. Roushangar K, Alizadeh F, Nourani V (2018) Improving capability of conceptual modeling of watershed rainfall–runoff using hybrid wavelet-extreme learning machine approach. J Hydroinform 20(1):69–87
- 40. Shaghaghi S, Bonakdari H, Gholami A, Ebtehaj I, Zeinolabedini M (2017) Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design. Appl Math Comput 313:271–286
- 41. Shiri J, Shamshirband S, Kisi O et al (2016) Prediction of water-level in the Urmia Lake using the extreme learning machine approach. Water Resour Manag 30(14):5217–5229
- 42. Tsai TM, Yen PH, Jiang MQ, Shieh YL (2010) Stream level forecasting in storm period by using self-organization algorithm coupled with distance level relation model. J Chin Inst Civ Hydraul Eng 22(4):363–374
- 43. Yadav B, Eliza K (2017) A hybrid wavelet-support vector machine model for prediction of Lake water level fluctuations using hydro-meteorological data. Measurement 103:294–301
- 44. Zaji AH, Bonakdari H (2014) Performance evaluation of two different neural network and particle swarm optimization methods for prediction of discharge capacity of modified triangular side weirs. Flow Meas Instrum 40:149–156
- 45. Zaji AH, Bonakdari H (2018) Robustness lake water level prediction using the search heuristic-based artificial intelligence methods. ISH J Hydraul Eng. https://doi.org/10.1080/09715010.2018.1424568
- 46. Zhang H, Liu X, Cai E, Huang G, Ding C (2013) Integration of dynamic rainfall data with environmental factors to forecast debris flow using an improved GMDH model. Comput Geosci 56:23–31
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-41f1d585-92c7-4a2d-8d79-b7c282ba34f3