Effectiveness of meta models of Gene Expression and Neural-Fuzzy Network Simulations in Hydrograph Modeling of Aquifer Representation

Document Type : Complete scientific research article

Authors

1 student

2 Graduated Master

3 Assistant Professor

Abstract

Underground water mapping is an effective tool for managing and protecting these resources, in order to apply a proper management to long-term planning and to better utilize the potential of the water in the plains. In this study, the monthly statistical data of the surface of piezometers for 5 years blue (89-88 to 93-92) related to the 8-pisometer level of the Lower-Andimeshk plain aquifer. At the beginning, using the Tesine method, the weighted average of each piezometer was obtained and the time series of the groundwater level of the plain, which represents the hydrograph of the representative water column of the study area, was calculated. Then, by using the neuro-fuzzy simulator and meta-model of the gene expression simulator, the hydrograph represents the modeling aquifer and the results were compared. The results showed that the meta-model of gene expression simulator with a coefficient of explanation of 7390.0 at the test stage was better than the neuro-fuzzy simulator model with a coefficient of explanation of 0.6348.Underground water mapping is an effective tool for managing and protecting these resources, in order to apply a proper management to long-term planning and to better utilize the potential of the water in the plains. In this study, the monthly statistical data of the surface of piezometers for 5 years blue (89-88 to 93-92) related to the 8-pisometer level of the Lower-Andimeshk plain aquifer. At the beginning, using the Tesine method, the weighted average of each piezometer was obtained and the time series of the groundwater level of the plain, which represents the hydrograph of the representative water column of the study area, was calculated. Then, by using the neuro-fuzzy simulator and meta-model of the gene expression simulator, the hydrograph represents the modeling aquifer and the results were compared. The results showed that the meta-model of gene expression simulator with a coefficient of explanation of 7390.0 at the test stage was better than the neuro-fuzzy simulator model with a coefficient of explanation of 0.6348.Underground water mapping is an effective tool for managing and protecting these resources, in order to apply a proper management to long-term planning and to better utilize the potential of the water in the plains. In this study, the monthly statistical data of the surface of piezometers for 5 years blue (89-88 to 93-92) related to the 8-pisometer level of the Lower-Andimeshk plain aquifer. At the beginning, using the Tesine method, the weighted average of each piezometer was obtained and the time series of the groundwater level of the plain, which represents the hydrograph of the representative water column of the study area, was calculated. Then, by using the neuro-fuzzy simulator and meta-model of the gene expression simulator, the hydrograph represents the modeling aquifer and the results were compared.

Keywords

References

1.Akbarzadeh, F., Hasanpour, H., and Emam Gholizadeh, S. 2016. The prediction of groundwater level in Shahrood plain using artificial neural network based on radial base function. J. Manage. Water. 13: 7. 104-118. (In Persian)
2.Barzegar, R., Fijani, E., Asghari Moghaddama, A., and Tziritis, E.
2017. Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. J. Sci. Total Environ. 599-600: 20-31.
3.Danandeh Mehr, A., and Majdzadeh Tabatabai, M.R. 2010. Prediction of Daily Discharge Trend of River Flow Based on Genetic Programming. J. Water Soil. 24: 2. 325-333. (In Persian)
4.Emam Gholizadeh, S., and Karimi Demne, R. 2017. Application of gene expression programming approach to estimate the aeration coefficient of bottom outlet gates of dams. J. Water Soil Cons. 24: 1. 279-286. (In Persian)
5.Ferreira, C. 2006. Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence). Ed n, editor. Springer-Verlag New York, Inc. Secaucus, NJ, USA.
6.Ferreira, C. 2001. Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst, 13: 2. 87-129.
7.Golabi, M.R., Akhond Ali, A.M., and Radmanesh, F. 2013. Comparison of the performance of different artificial neural network algorithms in seasonal rainfall modeling (Case study: Selected stations in Khuzestan province). J. Appl. Geosci. Res. 30: 13. 151-169. (In Persian)
8.Jang, J.S.R., Sun, C.T., and Mizutani, E. 1997. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall International. New Jersey.
9.Kişi, Ö. 2009. Evolutionary fuzzy models for river suspended sediment concentration estimation. J. Hydrol. 372 (1-4): 68-79.
10.Malekinezhad, H., and Pourshareyati, R. 2013. Application and comparison of cumulative time series model and artificial neural network model in prediction of groundwater level variation (Case study: Marvast plain). Irrigation Science & Engineering. 36: 3. 81-92. (In Persian)
11.Mattar, M.A., and Alamoud, A.I. 2015. Artificial neural networks for estimating the hydraulic performance of labyrinth-channel emitters. Computers and Electronics in Agriculture. 114: 189-201.
12.Meshkani, A., and Nazemi, A. 2009. Introduction to Data mining. Ferdowsi University of Mashhad. 456p. (In Persian
13.Nadiri, A., Naderi, K., Asghari Moghadam, A., and Habibi, M.H. 2016. Time and place prediction of groundwater level using artificial intelligence and ground statistics methods (Case study: Aqueduct of Duzduzan plain). Geograph. J. Plan. 20: 58. 2. 281-301. (In Persian)
14.Navabian, M., Liaghat, A., and Homari, M. 2003. Determination of soil saturated hydraulic conductivity using pedotransfer function. J. Agric. Engin. Res. 4: 16. 1-12. (In Persian)
15.Nayak, P.C., Sudheer, K.P., Rangan, D.M., and Ramasastri, K.S. 2004. A neuro-fuzzy computing technique for modeling hydrological time series. J. Hydrol. 291: 1-2. 52-66.
16.Nourani, V., and Salehi, K. 2008. Rainfall-runoff modeling using adaptive fuzzy neural network method and comparing it with neural network and fuzzy inference method Case study: (Lighvan Chay catchment area in East Aegean province). Fourth National Congress on Civil Engineering. University of Tehran. (In Persian)
17.Nourani, V., and Komasi, M. 2013. A geomorphology-based ANFIS model for multi-station modeling of rainfall-runoff process. J. Hydrol. 490: 41-55.
18.Pourmahammadi, S., Malekinezhad, H., and Pourshareyati, R. 2013. Comparison of the Efficiency of Neural Network Techniques and Time Series in Groundwater Forecasting (Case study: Bakhtegan Subzone of Fars Province). J. Water Soil Cons. Res. 20: 4. 251-262. (In Persian)
19.Rezaie-Balf, M., Naganna, S.R., Ghaemi, A., and Deka, P.C. 2017. Wavelet coupled MARS and M5 model tree approaches for groundwater level forecasting. J. Hydrol. 553: 356-373.
20.Ross, T.J. 1995. Fuzzy logic with engineering application. McGraw Hill Inc. USA.
21.Singh, V.P. Translation: Najafi, M.R. 2002. Hydrological systems for rainfall modeling. First volume. Tehran University Press. First Edition. 578p. (In Persian)
 
22.Stanley Raj, A., Hudson Oliver, D., Srinivas, Y., and Viswanath, J. 2017. Wavelet based analysis on rainfall and water table depth forecasting using Neural Networks in Kanyakumari district. Tamil Nadu, India. Groundwater for Sustainable Development. 5. 178-186.
23.Yoona, H., Hyunb, Y., Ha, K., Leec, K.K., and Kimd, G.B. 2016. A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions. Computers and Geosciences. 90: 144-155.
Journal of Water and Soil Conservation
Volume 25, Issue 4
November and December 2018
Pages 49-69

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