Comparative Evaluation of Numerical Model and Artificial Neural Network for Quantity and Quality Simulation of Najafabad Aquifer

Authors

1 PhD Student of Water Resource Management, Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Professor of Water Resource Management, Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

3 Prof. of Water Resource Management, Dept. of Civil Engineering, Isfahan University of Technology, Isfahan, Iran

Abstract

The aim of this study was to investigate the efficiency of the neural network model in quantitative and qualitative modeling of groundwater resources. For this purpose, the groundwater of the Najafabad aquifer located in Gavkhoni basin at the central plateau of Iran, was modeled using MODFLOW and MT3DMS modules of GMS v. 10 software. After calibrating and validating the model for a 11 years time period, the ranges of hydraulic conductivity, specific yield and longitudinal dispersivity coefficient were found to be 0.5-16 (m day-1), 0.023-0.113 and 7.5-18.2 (m), respectively. Then the study area divided into two sub-regions and the ANN model was designed for each of the sub-regions. Afterwards, the optimal parameters of the ANN models were determined using the 20-year dataset of water year and the genetic algorithm optimization model. Finally, calculated values relevant to the average level of groundwater and the mean concentration of TDS, which were acquired by the ANN model and the numerical model, were compared with the observed values from 2014 to 2016. Results showed that the neural network model is capable in simulating the quantitative and qualitative treatment of the groundwater system and can be used as a suitable alternative for the numerical model linking the management models.

Keywords

References

Alaviani F, Sedghi H, Moghaddam AA and Babazadeh H, 2018. Adopting GMS–PSO model to reduce groundwater withdrawal by integrated water resources management. International Journal of Environmental Research 12(5): 619-629.
Anonymous, 2010. Zayandehrood River Basin Report. Isfahan, Iran. (In Persian with English abstract)
Bahreini GR and Safavi HR, 2009.Water resources interaction modeling using satellite images and GIS techniques. Iran Water Resources Research 4(3):14-26. (In Persian with English abstract)
Chitzasan M and Sedghi MM, 2005. Physical modelling study of free and submerged flow on the obstructions in rivers with ten modes of transverse waves. 23th Earth Science Gathering. Tehran, Iran. (In Persian with English abstract)
Coppola EA, Rana AJ, Poulton MM, Szidarovszky F and Uhl VW, 2005. A neural network model for predicting aquifer water level elevations. Groundwater 43(2): 231-241.
Eshelman LJ and Shaffer DJ, 1993. Real-coded genetic algorithms and interval schemata. Foundations of Genetic Algorithms 2: 187-202. 
Kalantari M, 2016. Conjunctive management of groundwater and surface water using Honey-Bee mating algorithm. MSc Thesis, Isfahan University of Technology, Iran. (In Persian with English abstract)
Kardan Moghadam H and Banihabib ME, 2017. Investigation of interference of salt water in desert aquifers (case study: South Khorasan, Saryan aquifer). Water and Soil 31(3): 673-688. (In Persian with English abstract)
Kolahdoozan A, Dinpajooh Y, Mirabbasi Najafabadi R, Asadi E and Darbandi S, 2015. Effect of Zayandehrood river dry becoming on Najafabad aquifer oscillations. Iranian Journal of Soil and Water Research 46(1): 81-93. (In Persian with English abstract)
Lalehzari R and Abbaslou H, 2016. Simulating the effect of optimal water allocation on groundwater in monthly stress periods (Baghmalek plain, Khuzestan province). Water and Soil Science-University of Tabriz, 26(4.1): 307-320. (In Persian with English abstract)
Lalehzari R and Kerachian R, 2017. Quantity and quality modeling of groundwater flow in Shahrekord aquifer. Aquifer and Ganat 1(1): 26-37. (In Persian with English abstract)
Nadiri AA, Fijani E, Tsai FTC and Asghari Moghaddam A, 2013. Supervised committee machine with artificial intelligence for prediction of fluoride concentration. Journal of Hydroinformatics 15(4): 1474-1490.
Nadiri AA, Sadeghi Aghdam F and Asghari Moghaddam A, 2015. Using intelligence committee fuzzy logic model to estimate arsenic in water resources of Sahand dam basin. Geosiences Scientific Quarterly Journal 25(97): 397-406. (In Persian with English abstract)
Nayak PC, Rao YS and Sudheer KP, 2006. Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resources Management 20(1): 77-90.
Peralta RC, Forghani A and Fayad H, 2014. Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow. Journal of Hydrology 511: 776-785.
Sadeghfam S, Hassanzadeh Y, Khatibi R, Nadiri AA and Moazamnia M, 2019. Groundwater remediation through pump-treat-inject technology using optimum control by artificial intelligence (OCAI). Water Resources Management 33(3):1123-1145. 
Safavi HR and Enteshari S, 2016. Conjunctive use of surface and ground water resources using the ant system optimization. Agricultural Water Management 173: 23-34.
Sreekanth PD, Geethanjali N, Sreedevi PD, Ahmed S, Kumar NR and Jayanthi PK, 2009. Forecasting groundwater level using artificial neural networks. Current Science 933-939.
Vaheddoost B, Rezaie H and Behmanesh J, 2016. Effect of underground dam construction on discharge of qanats, using Modflow model (case study: Soofi-Makoo). Water and Soil Science-University of Tabriz, 26(4.1): 295-305. (In Persian with English abstract)
Water and Soil Science
Volume 31, Issue 1
March 2021
Pages 75-87

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