Volume 9, Issue 18 (1-2019)
jwmr 2019, 9(18): 56-69 |
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Eskandari A, Faramarzyan yasuj F, Solgi A, Zarei H. (2019). Evaluation of Combined ANFIS with Wavelet Transform to Modeling and Forecasting Groundwater Level. jwmr. 9(18), 56-69. doi:10.29252/jwmr.9.18.56
URL: http://jwmr.sanru.ac.ir/article-1-889-en.html
URL: http://jwmr.sanru.ac.ir/article-1-889-en.html
Shahid Chamran University of Ahvaz
Abstract: (3236 Views)
One of the most important factors, in a good management in any field, is having a proper perspective of the upcoming events. There is no exception in water resources management and the environment and awareness of the condition of water resources, in an area, plays a decisive role for planning water and agriculture. In this study, the Adaptive Neural Fuzzy Inference System (ANFIS) was used for the monthly forecast of Dalaki Basin groundwater levels in the province Bushehr in a 12-year period (2002- 2013). In order to improve the results of the model, the wavelet transform was used and the original signal was decomposed to sub-signals. Then, sub-signals were entered, as input, into ANFIS model to obtain the hybrid model, Wavelet-Adaptive Neural Fuzzy Inference System (WANFIS). To forecast the groundwater level of five observed wells has been used, using groundwater levels, precipitation, evaporation, andtemperature. Results showed that hybrid model, WANFIS, has better performance than ANFIS model. Also, it was showed that hybrid model has better performance in estimate extreme points. So, this method, using wavelet theory, increased the performance by 14%. At the end, groundwater levels were estimated by the best model in a year. The results of thepredicted groundwater levels showed that theincrease of having access to groundwater in the Dalaki area. This problem is noted to authorities of the area regarding the effects on water resources and the environment of the area.
Keywords: Hybrid model, Wavelet Transform, Groundwater Level, Dalaki basin, Modeling and Forecasting
Type of Study: Research |
Subject:
مديريت حوزه های آبخيز
Received: 2017/12/19 | Revised: 2019/01/20 | Accepted: 2018/05/29 | Published: 2019/01/21
Received: 2017/12/19 | Revised: 2019/01/20 | Accepted: 2018/05/29 | Published: 2019/01/21
References
1. Adamowski, J. and H.F. Chan. 2011. A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4): 28-40. [DOI:10.1016/j.jhydrol.2011.06.013]
2. Afruzi, A., Zare Abyaneh, H. 2017. Groundwater Level Modeling and Forecasting using the Time Series Models (Case Study: The Plains of Hamadan Province). Journal of Watershed Management Research, 8(15): 102-111 (In Persian).
3. Ebrahimi, H., T. Rajaee. 2017. Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Global and Planetary Change, 148:181-191. [DOI:10.1016/j.gloplacha.2016.11.014]
4. Fallah-Mehdipour, E., O. Bozorg Haddad and M.A. Mariño. 2013. Prediction and simulation of monthly groundwater levels by genetic programming. Journal of Hydro-Environment Research, 7(4): 253-260. [DOI:10.1016/j.jher.2013.03.005]
5. Izadi, A., K. Davari, A. Alizadeh, B. Ghahreman, V. Haghayeghi and S.A. Moghaddam. 2007. Estimation groundwater Level Using Artificial Neural Network. Irrigation and Drainage Journal of Iran, 71(2): 1-59 (In Persian).
6. Jang, J.S.R., C.T. Sun, E. Mizutani. 1997. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall International, New Jersey. [DOI:10.1109/TAC.1997.633847]
7. Kişi, Ö. 2009. Evolutionary fuzzy models for river suspended sediment concentration estimation. Journal of Hydrology, 372(1-4): 68-79. [DOI:10.1016/j.jhydrol.2009.03.036]
8. Mallat, S.G. 1998. A wavelet tour of signal processing, San Diego, 123 pp. [DOI:10.1016/B978-012466606-1/50008-8]
9. Moosavi, V., M. Vafakhah, B, Shirmohammadi and M. Ranjbar. 2014. Optimization of Wavelet-ANFIS and Wavelet-ANN Hybrid Models by Taguchi Method for Groundwater Level Forecasting. Arabian Journal for Science and Engineering, 39: 1785-1796. [DOI:10.1007/s13369-013-0762-3]
10. Nakhai, M., A. Saberi Nasr and R. Farajzadeh. 2011. Advantages of Neural-Wavelet Network in Forecasting of Groundwater Fluctuations. Fourth Conference of Iranian Water Resources Management. Amirkabir University of Technology Tehran, (In Persian).
11. Nayak, P., Y.R. Satyaji Rao and K.P. Sudheer. 2006. Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach. Water Resources Management, 20: 77-90. [DOI:10.1007/s11269-006-4007-z]
12. Nayak, P.C., K.P. Sudheer, D.M. Rangan and K.S. Ramasastri. 2004. A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291(1-2): 52-66. [DOI:10.1016/j.jhydrol.2003.12.010]
13. Nourani, V., A. Asghari Mogaddam and A.O. Naderi. 2008. An ANN-based model for spatiotemporal groundwater level forecasting. Hydrological Processes, 22: 5054-5066. [DOI:10.1002/hyp.7129]
14. Nourani, V., M. Komasi and A. Mano. 2009. A Multivariate ANN-Wavelet Approach for Rainfall-Runoff Modeling. Water Resources Management, 23: 2877-2894. [DOI:10.1007/s11269-009-9414-5]
15. Nourani, V. and S. Mousavi. 2016. Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method. Journal of Hydrology, 536: 10-25. [DOI:10.1016/j.jhydrol.2016.02.030]
16. Rajai, T. and A. Zenivand. 2014. Modeling groundwater level using a wavelet hybrid model- artificial neural network (case study: Sharif Abad plain). Civil Engineering and Environment Journal, 44(3): 77: 63-51 (In Persian).
17. Ramezani-Charmahineh, A. and M. Zounemat-Kermani. 2017. Evaluation of the Efficiency of Support Vector Regression, Multi-Layer Perceptron Neural Network and Multivariate Linear Regression on Groundwater Level Prediction (Case Study: Shahrekord Plain). Journal of Watershed Management Research, 8(15): 1-12 (In Persian).
18. Riad, S., J. Mania, L. Bouchaou and Y. Najjar. 2004. Rainfall-runoff model usingan artificial neural network approach. Mathematical and Computer Modelling, 40(7-8): 839-846. [DOI:10.1016/j.mcm.2004.10.012]
19. Shiri, J., O. Kisi, H. Yoon, K.K. Lee and A. Hossein Nazemi. 2013. Predicting groundwater level fluctuations with meteorological effect implications-A comparative study among soft computing techniques. Computers & Geosciences, 56(0): 32-44. [DOI:10.1016/j.cageo.2013.01.007]
20. Solgi, A. 2014. Predict river flow with hybrid model wavelet-artificial neural network and compraction it with adaptive neuro fuzzy inference system and artificial neural network (case study: Nahavand Gamasiyab river). M.Sc.Thesis, Shahid Chamran University of Ahvaz, Ahvaz, Iran, 164 pp (In Persian).
21. Suryanarayana, C., C. Sudheer, V. Mahammood and B.K. Panigrahi. 2014. An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing, 145: 324-335. [DOI:10.1016/j.neucom.2014.05.026]
22. Toolbox of the MATLAB software, R2013a.
23. Wang, W. and J. Ding. 2003. Wavelet Network Model and Its Application to the Prediction of Hydrology. Natureand Science, 1(1): 67-71.
24. Zadeh, L.A. 1965. Fuzzy Sets. Information and control, 8(3): 338-353. [DOI:10.1016/S0019-9958(65)90241-X]
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