Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction

Water Resources Management

Volumn 31, Issue 7, 2017, Pages 2141-2156

  Moeeni, Hamid ^a   Bonakdari, Hossein ^a   Ebtehaj, Isa ^a  

^a RAZI UNIVERSITY   (Iran)

Author keywords

Forecasting; Hybrid model; Inflow; SARIMA; Soft computing

Indexed keywords

DATA HANDLING; DEEP NEURAL NETWORKS; FORECASTING; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; NEURAL NETWORKS; PROCESSING; SOFT COMPUTING; WATER RESOURCES;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; HYBRID MODEL; INFLOW; NONLINEAR COMPONENTS; SARIMA; SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGES; SOFT COMPUTING METHODS; WATER RESOURCE PLANNING;

FUZZY INFERENCE;

ARTIFICIAL NEURAL NETWORK; FORECASTING METHOD; FUZZY MATHEMATICS; INFLOW; NUMERICAL MODEL; PREDICTION; WATER PLANNING; WATER RESOURCE;

EID: 85028251765     PISSN: 09204741     EISSN: 15731650     Source Type: Journal    
DOI: 10.1007/s11269-017-1632-7     Document Type: Article

References (39)

1. Asadi S, Shahrabi J, Abbaszadeh P, Tabanmehr S (2013) A new hybrid artificial neural networks for rainfall–runoff process modeling. Neurocomputing 121:470–480
2. Atiaa AM (2015) Modeling of stage-discharge relationship for Gharraf River, southern Iraq by using data driven techniques: a case study. Water Utility J 9:31–46
3. Barman S, Bhattacharjya RK (2015) Comparison of linear regression, non-linear regression and artificial neural network model for downscaling of rainfall at Subansiri river basin, Assam, India. Eur Water 51:51–62
4. Cadenas E, Rivera W (2010) Wind speed forecasting in three different regions of Mexico using a hybrid ARIMA-ANN model. Renew Energ 35:2732–2738
5. Chebaane M, Salas JD, Boes DC (1995) Product periodic autoregressive processes for modeling intermittent monthly streamflows. Water Resour Res 31:1513–1518
6. Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230:244–257
7. Ebtehaj I, Bonakdari H (2013) Evaluation of sediment transport in sewer using artificial neural network. Eng Appl Comput Fluid Mech 7(3):382–392
8. Ebtehaj I, Bonakdari H (2014) Performance evaluation of adaptive neural fuzzy inference system for sediment transport in sewer. Water Resour Manag 28:4765–4779
9. Faruk DÖ (2010) A hybrid neural network and ARIMA model for water quality time series prediction. Eng Appl Artif Intell 23:586–594
10. Firat M (2008) Comparison of artificial intelligence techniques for river flow forecasting. Hydrol Earth Syst Sci 12:123–139
11. Haykin SS (2009) Neural networks and learning machines. Pearson education upper Saddle River, New Jersey, USA
12. He Z, Wen X, Liu H, Du J (2014) 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 509:379–386
13. Jain A, Kumar AM (2007) Hybrid neural network models for hydrologic time series forecasting. Appl Soft Comput 7:585–592
14. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Manage Cybernet 23(3):665–685
15. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. [book review]. IEEE Trans Autom Control 42:1482–1484
16. Jeong K, Koo C, Hong T (2014) An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network). Energy 71:71–79
17. Kavousi Fard A, Akbari Zadeh MR (2014) A hybrid method based on wavelet ANN and ARIMA model for short-term load forecasting. J Exp Theor Artif In 26:167–182
18. Khashei M, Bijari M (2011) A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl Soft Comput 11:2664–2675
19. Kisi Ö, Cigizoglu HK (2007) Comparison of different ANN techniques in river flow prediction. Civ Eng Environ Syst 24:211–231
20. Kumar S, Tiwari MK, Chatterjee C, Mishra A (2015) Reservoir inflow forecasting using ensemble models based on neural networks wavelet analysis and bootstrap method. Water Resour Manag 29:4863–4883
21. Liu H, Tian HQ, Li YF (2012) Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Appl Energ 98:415–424
22. Moeeni H, Bonakdari H (2016) Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model. Stoch Env Res Risk A. doi:10.1007/s00477-016-1273-z
23. Moeeni H, Bonakdari H, Ebtehaj I (2017b) Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach. J Earth Syst Sci 126:1–13
24. Moeeni H, Bonakdari H, Fatemi SE, Zaji AH (2017a) Assessment of stochastic models and a hybrid artificial neural network-genetic algorithm method in forecasting monthly reservoir inflow. INAE Lett. doi:10.1007/s41403-017-0017-9
25. Mohan S, Vedula S (1995) Multiplicative seasonal ARIMA model for longterm forecasting of inflows. Water Resour Manag 9:115–126
26. Nawaz N, Harun S, Talei A (2015) Application of adaptive network-based fuzzy inference system (ANFIS) for river stage prediction in a tropical catchment. Appl Mech Mater: Trans Tech Publ 735:195–199
27. Pandhiani SM, Shabri AB (2015) Time series forecasting by using hybrid models for monthly streamflow data. Appl Math Sci 9:2809–2829
28. Pektas AO, Cigizoglu HK (2013) ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient. J Hydrol 500:21–36
29. Ruiz-Aguilar J, Turias I, Jiménez-Come M (2014) Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting. Transport Res E-Log 67:1–13
30. Ruiz-Aguilar J, Turias I, Jiménez-Come M (2015) A novel three-step procedure to forecast the inspection volume. Transport Res C-Emer 56:393–414
31. Salas JD, Chung CH, Cancelliere A (2005) Correlations and crossing rates of periodic-stochastic hydrologic processes. J Hydrol Eng 10:278–287
32. Valipour M (2015) Long-term runoff study using SARIMA and ARIMA models in the United States. Meteorol Appl 22:592–598
33. Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the ARMA, ARIMA and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J Hydrol 476:433–441
34. Voronin S, Partanen J (2014) Forecasting electricity price and demand using a hybrid approach based on wavelet transform ARIMA and neural networks. Int J Energ Res 38:626–637
35. Wang KH, Altunkaynak A (2011) Comparative case study of rainfall-runoff modeling between SWMM and fuzzy logic approach. J Hydrol Eng 17:283–291
36. Wang WC, Chau KW, Xu DM, Chen XY (2015b) Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resour Manag 29:2655–2675
37. Wang J, Hu J, Ma K, Zhang Y (2015a) A self-adaptive hybrid approach for wind speed forecasting. Renew Energ 78:374–385
38. Yurekli K, Kurunc A, Ozturk F (2005) Application of linear stochastic models to monthly flow data of Kelkit stream. Ecol Model 183:67–75
39. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175