Watershed Engineering and Management

In collaboration with Iranian Watershed Management Association

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Publication Ethics

Indexing and Abstracting

FAQ

Peer Review Process

Guide for Authors

Receiving the code ORCID

Reviewers

Publons membership guide

The Estimation of groundwater level changes using four different techniques of evolutionary neural network, case study of Dasht-e-Abbas plain, Ilam province

Document Type : Research Paper

Authors

1 . Student of Computer Science, Department of Computer, , Islamic Azad University Kermanshah,

2 Student of Irrigation and Drainage Engineering, Department of Irrigation and Drainage, Arak University

Abstract

Abstract
In dry and semi-arid areas, water is the most factor of limiter in agriculture. In these areas, due to the lack of surface flows, major pressures enter on groundwater. Groundwater resources in the studied area (Dashte-Abbas plain) also suffered a severe drop in surface water due to unplanned use. In this study, we compared four different models of evolutionary neural network, a multi-layered perceptron neural network with Genetic Algorithm (ANN-GA), a multilayered perceptron neural network with particle swarm optimization (ANN-PSO), a multilevel perceptron neural network with Imperialism competitive algorithm (ANN-ICA) and multi-layered perceptron neural network with ant colony optimization (ANN-ACOR) for estimating groundwater level according to groundwater inflow, effective penetration of rainfall, effective penetration of surface flow and flood, effective penetration of return water Agriculture, underground outflow, withdrawal from aquifer for agriculture, evaporation from groundwater level and past groundwater data Were used. groundwater level comparisons are the combination of inputs has been prepared using Auto-correlation analysis, partial Auto-correlation and cross-correlation for each model. Optimal models are obtained by changing the control parameters. The best results are obtained from the input models (GWLt-1, GWLt-2, Qint, Qpt-1, Qrt-1, Qit-1, Qoutt-1, Qwt-1, and Qet-1). The accuracy of the mean squared error in the test phase for ANN-PSO, ANN-ICA, ANN-ACOR models was 1.2208, 0.9456 and 1.7720, respectively, and for the ANN-GA model, it was 0.8739. The mean relative error of ANN-GA model is 3.6% and its determined coefficient is 0.9388. According to the results, the ANN-GA model showed better performance than the other three models for estimating groundwater level.

Keywords

References
  1. Abd-Elazim, S.M. and E.S. Ali. Imperialist competitive algorithm for optimal STATCOM design in a multimachine power system. International Journal of Electrical Power and Energy Systems, 76 pages.
  2. Acharya, N., N.A. Shrivastava, B.K. Panigrahi and U.C. Mohanty. 2014. Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine. Climate Dynamics, 43(5-6): 1303-1310.
  3. 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.
  4. Affandi, A.K. and K. Watanabe. 2007. Daily groundwater level fluctuation forecasting using soft computing technique. Nature and Science, 5(2): 1-10.
  5. Amiri, M., J. Ghiasi-Freez, B. Golkar and A. Hatampour. 2015. Improving water saturation estimation in a tight shaly sandstone reservoir using artificial neural network optimized by imperialist competitive algorithm, a case study. Journal of Petroleum Science and Engineering, 127: 347-358.
  6. Atashpaz-Gargari, E. and C. Lucas. 2007. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In Evolutionary Computation, CEC 2007, IEEE Congress on Evolutionary Computation, pages 4661-4667.
  7. Alizadeh, A. 2001. Principles of applied hydrology. Astan Quds Razavi, Mashhad, 808 pages (in Persian).
  8. Basheer, I.A. and M. Hajmeer. 2000. Artificial neural networks: fundamentals, computing, design and application. Journal of Microbiological Methods, 43(1): 3-31.            
  9. Bhattacharyya, S. and P.C. Pendharkar. 1998. Inductive, evolutionary and neural computing techniques for discrimination: a comparative study. Decision Sciences, 29(4): 871-899.
  10. Chau, K.W. 2006. Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. Journal of Hydrology, 329(3-4): 363-367.
  11. Daliakopoulos, I.N., P. Coulibaly and I.K. Tsanis. 2005. Groundwater level forecasting using artificial neural networks. Journal of Hydrology, 309(1-4): 229-240.
  12. Dash, N.B., S.N. Panda, R. Remesan and N. Sahoo. 2010. Hybrid neural modeling for groundwater level prediction. Neural Computing and Applications, 19(8): 1251-1263.
  13. Dorigo, M. and L.M. Gambardella. 1997. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1): 53-66.
  14. Dorigo, M., M. Birattari and T. Stutzle. 2006. Artificial ants as a computational intelligence technique. IEEE Computational Intelligence Magazine, 1: 28-39.
  15. Eberhart, R.C. and Y. Shi. 1998. Comparison between genetic algorithms and particle swarm optimization. In International Conference on Evolutionary Programming, Springer, Berlin, Heidelberg, pages 611-616.
  16. Gaur, S., Ch.S.D. Graillot, B.R. Chahar and D.N. Kumar. 2013. Application of artificial neural networks and particle swarm optimization for the management of groundwater resources. Water Resources Management, 27(3): 927-941.       
  17. Ghaedi, M., A.M. Ghaedi, E. Negintaji, A. Ansari and F. Mohammadi. 2014. Artificial neural network–imperialist competitive algorithm based optimization for removal of sunset yellow using Zn(OH)2 nanoparticles-activated carbon. Journal of Industrial and Engineering Chemistry, 20(6): 4332-4343
  18. Hou, L., X.S. Wang, B.X. Hu, J. Shang and L. Wan. 2016. Experimental and numerical investigations of soil water balance at the hinterland of the Badain Jaran Desert for groundwater recharge estimation. Journal of Hydrology, 540: 386-396.
  19. Iwalewa, T.M., A.S. Elamin and S.I. Kaka. 2016. A coupled model simulation assessment of shallow water-table rise in a Saudi Arabian coastal city. Journal of Hydro-Environment Research, 12: 46-58.                 
  20. Jalalkamali, A. and N. Jalalkamali. 2011. Groundwater modeling using hybrid of artificial neural network with genetic algorithm. African Journal of Agricultural Research, 6(26): 5775-5784.
  21. Khalil, B., S. Broda, J. Adamowski, B. Ozga-Zielinski and A. Donohoe. 2015. Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models. Hydrogeology Journal, 23(1): 121-141.
  22. Kişi, Ö. 2007. Streamflow forecasting using different artificial neural network algorithms. Journal of Hydrologic Engineering, 12(5): 532-539.
  23. Kisi, O., H. Sanikhani, M. Zounemat-Kermani and F. Niazi. 2015. Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Computers and Electronics in Agriculture, 115: 66-77.      
  24. Kisi, O., M. Tombul and M.Z. Kermani. 2015. Modeling soil temperatures at different depths by using three different neural computing techniques. Theoretical and Applied Climatology, 121(1-2): 377-387.
  25. Kisi, O., M. Alizamir and M. Zounemat-Kermani. 2017. Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data. Natural Hazards, 87(1): 367-381.
  26. Kuo, R.J., C.H. Chen and Y.C. Hwang. 2001. An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems, 118(1): 21-45.
  27. Mohanty, S., M.K. Jha, S.K. Raul, R.K. Panda and K.P. Sudheer. 2015. Using artificial neural network approach for simultaneous forecasting of weekly groundwater levels at multiple sites. Water Resources Management, 29(15): 5521-5532.
  28. Mukherjee, I. and S. Routroy. 2012. Comparing the performance of neural networks developed by using Levenberg–Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process. Expert Systems with Applications, 39(3): 2397-2407.
  29. 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.
  30. Nayak, P.C., Y.S. Rao and K.P. Sudheer. 2006. Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resources Management, 20(1): 77-90.
  31. Nazari-Shirkouhi, S., H. Eivazy, R. Ghodsi, K. Rezaie and E. Atashpaz-Gargari. 2010. Solving the integrated product mix-outsourcing problem using the imperialist competitive algorithm. Expert Systems with Applications, 37(12): 7615-7626.
  32. Samani, N., M. Gohari-Moghadam and A.A. Safavi. 2007. A simple neural network model for the determination of aquifer parameters. Journal of Hydrology, 340(1-2): 1-11.
  33. Shen, Q., J.H. Jiang, J.C. Tao, G.L. Shen and R.Q. Yu. 2005. Modified ant colony optimization algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors. Journal of Chemical Information and Modeling, 45(4): 1024-1029.
  34. Socha, K. and M. Dorigo. 2008. Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3): 1155-1173.
  35. Shen, C., L. Wang and Q. Li. 2007. Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. Journal of Materials Processing Technology, 183(2-3): 412-418.
  36. Sun, Y., D. Wendi, D.E. Kim and S.Y. Liong. 2016. Application of artificial neural networks in groundwater table forecasting, a case study in a Singapore swamp forest. Hydrology and Earth System Sciences, 20(4): 1405-1412.
  37. Toksarı, M.D. 2007. Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy, 35(8): 3984-3990.
  38. Tahershamsi, A. and R. Sheikholeslami. 2011. Optimization to identify Muskingum model parameters using imperialist competitive algorithm. Iran University of Science and Technology, 1(3): 475-484.
  39. Wong, F.S. 1991. Time series forecasting using backpropagation neural networks. Neurocomputing, 2(4): 147-159.
  40. Xi, Z., Y. Zhang and C. Zhu. 2012. Application of PSO-neural network model in prediction of groundwater level in Handan City. International Journal on Advances in Information Sciences and Service Sciences, 4(6): 177-183.
  41. Yoon, H., S.C. Jun, Y. Hyun, G.O. Bae and K.K. Lee. 2011. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology, 396(1-2): 128-138.
  42. Zeng, X., M. Ye, J. Burkardt, J. Wu, D. Wang and X. Zhu. 2016. Evaluating two sparse grid surrogates and two adaptation criteria for groundwater Bayesian uncertainty quantification. Journal of Hydrology, 535: 120-134.                                                                                                                           
  43. Zounemat-Kermani, M. 2012. Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature. Meteorology and Atmospheric Physics, 117(3-4): 181-192.
  44. Zounemat-Kermani, M., O. Kisi and T. Rajaee. 2013. Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff. Applied Soft Computing, 13(12): 4633-4644.
Watershed Engineering and Management
Volume 12, Issue 3
September 2020
Pages 737-755
Files
Share
How to cite
Statistics