Comparison of Optimal Artificial Neural Network Models for Groundwater Nitrate Simulation (Case Study: Behbahan Plain)
محورهای موضوعی : شبکه های عصبی و یادگیری عمیقAtefeh Sayadi Shahraki 1 , Fahimeh Sayadi Shahraki 2 , Bijan Haghighati 3
1 - Soil and Water Research Department, Chaharmahal and Bakhtiari Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shahrekord, Iran
2 - Department of Electrical Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran
3 - Soil and Water Research Department, Chaharmahal and Bakhtiari Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shahrekord, Iran
کلید واژه: Artificial Neural Network, Genetic Algorithm, Nitrate, Particle Swarm Optimization Algorithm, Simulation,
چکیده مقاله :
Groundwater is the most important water resource for drinking and agricultural usage especially in arid and semi-arid regions. So, it is important to note its quality. Nitrate is one of the groundwater pollutants which is mostly derived from agricultural and wastewater sources. Since nitrate determination using sampling was very expensive and limited, it is necessary to use new prediction methods like artificial neural network. The use of artificial neural networks in hydrological studies of the last decade shows that these models have a high ability to discover the relationship between data and recognize patterns. The success of neural network models in estimating different parameters of water sources has always been emphasized by different researchers.
Groundwater is the most important water resource for drinking and agricultural usage especially in arid and semi-arid regions. So, it is important to note its quality. Nitrate is one of the groundwater pollutants which is mostly derived from agricultural and wastewater sources. Since nitrate determination using sampling was very expensive and limited, it is necessary to use new prediction methods like artificial neural network. The use of artificial neural networks in hydrological studies of the last decade shows that these models have a high ability to discover the relationship between data and recognize patterns. The success of neural network models in estimating different parameters of water sources has always been emphasized by different researchers.
1. Clark, I. and Fritz, P. 1997. Environmental Isotopes in Hydrogeology. 1st Edition. CRC Press. P. 300-342.
2. Khodaei, K., Mohammadzadeh, H., Naseri, H.R. and Shahsavari, A.A. 2012. Investigating nitrate pollution in the Dezful-Andimeshk plain and determining the source of pollution using N15 and O18 isotopes. Iranian Geology Quarterly, 22: 93-111. (presian)
3. Keeney D. 1986 Aug.11-13. Nitrate in ground water: Agricultural contribution and control. Proceedings of the Agricultural Impacts on Ground Water Conference. p. 329-351.
4. Goulding, K. 2000. Nitrate leaching from arable and horticultural land. Soil Use and Management. 16 (1): 145–151.
5. U.S. EPA. Drinking water standards. U.S. EPA. 816-F-00- 995. 1995. Pp. 4.
6. Najah, A., Elshafie, A., Karim, OA. and Jaffar, O. 2003. Prediction of Johor river water quality parameters using artificial neural networks. European Journal of Scientific Research. 2009; 28 (3):422-435.
7. Asadollahfardi, A., Taklifi Gh. and Ghanbari A. 2012. Application of artificial neural network to predict TDS in Talkheh Rud River. Journal of Irrigation and Drainage Engineering. 138(4):363–370.
8. Musavi-Jahromi, SH. and Golabi M. 2008. Application of artificial neural networks in the river water quality modeling: Karoon river, Journal of Applied Sciences. 8 (12):2324-2328.
9. Ramasamy, N., Krishnan, P., Bemard, J. and Ritter, W. 2003. Modeling Nitrate Concentration in Ground Water Using Regression and Neural Networks. FREC Research Reports. Department of Food and Resource Economics University of Delaware.
10. Yesilnacar,M.I., Sahinkaya E., Naz M. and Ozkaya B. 2008. Neural network prediction of nitrate in groundwater of Harran Plain, Turkey. Environ Geol. 56:19–25.
11. Banejad, H., Kamali, Mahsa., Amirmoradi, Kimia. and Olyaie, F. (2013). Forecasting Some of the Qualitative Parameters of Rivers Using Wavelet Artificial Neural Network Hybrid (W-ANN) Model (Case of study: Jajroud River of Tehran and Gharaso River of Kermanshah). Journal Health & Environ, 6(3), 277-294. (persian)
12. Mirzavand, M., Sadati Nrjad, M. and Akbari, M. (2015). Simulation Changes in groundwater quality with artificial neural network model (Case study: Kashan aquifer). Iranian Journal of Natural Resources, 68 (1), 159-171. (persian)
13. Shiri, J., and Kisi, O. 2011. Comparison of genetic programming with neurofuzzy systems for predicting short-term water table depth fluctuations. Comput. Geosci. 37 (10): 1692-1701.
14. Traore, S. and Guven, A. 2012. Regional-specific numerical models of evapotranspiration using gene-expression programming interface in Sahel. Water Resour. Manag. 26 (15): 4367-4380.
15. Moashrei, S.A., Tabatabaie, S.M., Razaghi, P., Sarani, N., and Eslami Mahdi Abadi, S.H. (2012). “Estimating the groundwater nitrate by using artificial neural network and optimizing it by genetic algorithm.” Proc. Environment and Civil Engineering, Kuala Lumpur, Malaysia, 85-92.
16. Stuart, M.E., Gooddy, D.C., Bloomfield, J.P. and Williams, A.T. 2011. A review of the impact of climate change on future nitrate concentrations in groundwater of the UK. Science of the Total Environment. 409 (15): 2859-2873.
17. Sayadi Shahraki, A., Naseri, A.A. and Soltani Mohammadi, A. 2020. Simulation of hydraulic head using Particle Swarm Optimization Algorithm and Genetic Algorithm. (Case study: Debal khazaie sugarcane plantation). Journal of Water Resources Engineering. 12 (43): 14-24. (In Persian with English abstract).
18. Eberhart, R. and Shi Y. 16-19 Jul 2000. Comparing inertia weights and constriction factors in particle swarm, in: Proceedings of the Congress on Evolutionary Computation, La Jolla. p. 84–88.
19. Goldberg, D.E. 1989. Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley. 1st Edition. p. 1-77.
20. Dayhoff, J. E. 1990. Neural Network Principles. Prentice-Hall International, U.S.A.
21. Khanna, T. 1990. Foundation of neural networks. Addison-Wesley Publishing Company, U.S.A.
22. Iranian National Standard No. 1053: 1376, physical and chemical characteristics of drinking water. (presian)
23. Saha, L.C. and Kumar, S. 1990. Comparative quality of potable waters at Bhagalpur. India Acta Hydrochim Hydrobiol. 18(4):459–467.
24. Hamilton, P. A. and Helsel, D.R. 1995. Effects of agriculture on groundwater quality in five regions of the United States, Ground Water. 33(6):217–226.
25. Alighadri, M., Hazrati, S., Sakhaeizadeh, A. and Soleimanpour, M. 2011. Nitrate concentration measurement in Ardabil drinking water supply sources and distribution network, Ardabil Health and Health Journal, 2 (2): 69-75. (presian)
26. Yazdanbakhsh, A.R., Mohammadi, H., Sheikhmohammadi, A., Bonyadinejad, R. and Ghanbari, Gh. 2009. Investigating the concentration of nitrite and nitrate in the drinking water of Tehran in the areas covered by Shahid Beheshti University of Medical Sciences, the 12th Iran Environmental Health Conference. Shahid Beheshti University of Medical Sciences. (presian)
27. Fazeli, M., Kalantari, N., Rahimi, M.H. and Khoubyari, A. 2011. Investigating the temporal and spatial distribution of Nitrate contamination of underground water sources in the Zidon Plain, Water Resources Engineering Journal, 4: 45-51. (presian)
