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KSCE Journal of Civil Engineering

, Volume 23, Issue 12, pp 5235–5243 | Cite as

Prediction of Ground Water Table Using NF-GMDH Based Evolutionary Algorithms

  • Amir-Abbas Jahanara
  • Saeed Reza KhodashenasEmail author
Water Resources and Hydrologic Engineering
  • 2 Downloads

Abstract

Groundwater, as the key element of water resources, can play inevitably substantial role in managing groundwater aquafers. In fact, a ferocious demand for acquiring precise estimation of groundwater table is of remarkable significance for analyzing water resources systems. A wide range of artificial intelligence techniques were used to predict groundwater table with highly convincing level of precision. Hence, this investigation aims to present an integration of a neuro-fuzzy (NF) system and group method of data handling (GMDH) in order to forecast the ground water table (GWT). The NF-GMDH network has been improved by means of the particle swarm optimization (PSO) and gravitational search algorithm (GSA) as evolutionary algorithms. The proposed methods were developed using records of two wells in Illinois State, USA. For this purpose, datasets related to time series of GWT have been grouped into three sections: training, testing, and validation phases. Through training and testing phases, the efficiency of the NF-GMDH methods were studied. The performances of proposed techniques were compared to the performance of radial basis function-neural network (RBF-NN). Evaluation of statistical results indicated which NF-GMDH-PSO network (R = 0.973 and RMSE = 0.545) is capable of providing higher level of precision rather than the NF-GMDH-GSA network (R = 0.969 and RMSE = 0.618) and RBF-NN (R = 0.814 and RMSE = 1.41). Also, conducting an external validation for the improved NF-GMDH models showed the most permissible level of precision.

Keywords

ground water table group method of data handling evolutionary algorithms fuzzy systems 

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References

  1. Azimi, H., Bonakdari, H., Ebtehaj, I., Gharabaghi, B., and Khoshbin, F. (2018). “Evolutionary design of generalized group method of data handling-type neural network for estimating the hydraulic jump roller length.” Acta Mechanica, Vol. 229, No. 3, pp. 1197–1214, DOI:  https://doi.org/10.1007/s00707-017-2043-9.CrossRefGoogle Scholar
  2. Bateni, S. M. Borghei, S. M., and Jeng, D. S. (2007). “Neural network and neuro-fuzzy assessments for scour depth around bridge piers.” Engineering Applications of Artificial Intelligence, Vol. 20, pp. 401–414, DOI:  https://doi.org/10.1016/j.engappai.2006.06.012.CrossRefGoogle Scholar
  3. Coppola, E., Rana, A., Poulton, M., Szidarovszky, F., and Uhl, V. (2005). “A neural network model for predicting aquifer water level elevations.” Ground Water, Vol. 43, No. 2, pp. 231–241, DOI:  https://doi.org/10.1111/j.1745-6584.2005.0003.x.CrossRefGoogle Scholar
  4. Coulibaly, P., Anctil, F., Aravena, R., and Bobee, B. (2001). “Artificial neural network modeling of water table depth fluctuations.” Water Resources Research, Vol. 37, No. 4, pp. 885–896, DOI:  https://doi.org/10.1029/2000WR900368.CrossRefGoogle Scholar
  5. Ebtehaj, I., Bonakdari, H., Khoshbin, F., Bong, C. H. J., and Ghani, A. A. (2017). “Development of group method of data handling based on genetic algorithm to predict incipient motion in rigid rectangular storm water channel.” Scientia Iranica. Transaction A, Civil Engineering, Vol. 24, No. 3, pp.1000–1009, DOI:  https://doi.org/10.24200/SCI.2017.4083.Google Scholar
  6. Fallah-Mehdipour, E., Bozorg Haddad, O., and Mariño, M. A. (2014). “Genetic programming in groundwater modeling.” Journal of Hydrologic Engineering, Vol. 19, No. 12, p. 04014031, DOI:  https://doi.org/10.1061/(ASCE)HE.1943-5584.0000987.CrossRefGoogle Scholar
  7. Ivakhnenko, A. G. (1971). “Polynomial theory of complex systems.” IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-1, No. 4, pp. 364–378, DOI:  https://doi.org/10.1109/TSMC.1971.4308320.MathSciNetCrossRefGoogle Scholar
  8. Kennedy, J. and Eberhart, R. (1995). “Particle swarm optimization.” Proceedings of International Conference on Neural Networks, 27 Nov-1 Dec, Perth, Australia, pp. 1942–1948, DOI:  https://doi.org/10.1109/ICNN.1995.488968.CrossRefGoogle Scholar
  9. Kisi, O., Alizamir, M., and Zounemat-Kermani, M. (2017). “Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data.” Natural Hazards, Vol. 87, No. 1, pp. 367–381, DOI:  https://doi.org/10.1007/s11069-017-2767-9.CrossRefGoogle Scholar
  10. Kisi, O. and Shiri, J. (2012). “Wavelet and neuro-fuzzy conjunction model for predicting water table depth fluctuations.” Hydrology Research, Vol. 43, No. 3, pp. 286–300, DOI:  https://doi.org/10.2166/nh.2012.104b.CrossRefGoogle Scholar
  11. Madala, H. R. and Ivakhnenko, A. G. (1994). “Inductive learning algorithms for complex systems modeling.” CRC Press, Boca Raton, FL, USA.Google Scholar
  12. Mitrakis, N. E. and Theocharis, J. B. (2012). “A diversity-driven structure learning algorithm for building hierarchical neuro-fuzzy classifiers.” Information Sciences, Vol. 186, No. 1, pp. 40–58, DOI:  https://doi.org/10.1016/j.ins.2011.09.035.CrossRefGoogle Scholar
  13. Mohammadrezapour, O., Kisi, O., and Pourahmad, F. (2018). “Fuzzy cmeans and K-means clustering with genetic algorithm for identificationof homogeneous regions of groundwater quality.” Neural Computingand Applications, Springer, pp. 1–13, DOI:  https://doi.org/10.1007/s00521-018-3768-7.Google Scholar
  14. Naderianfar, M., Piri, J., and Kisi, O. (2017). “Pre-processing data to predict groundwater levels using the fuzzy standardized evapotranspiration and precipitation index (SEPI).” Water Resources Management, Vol. 31, No. 14, pp. 4433–4448, DOI:  https://doi.org/10.1007/s11269-017-1757-8.CrossRefGoogle Scholar
  15. Najafzadeh, M. (2015). “Neuro-fuzzy GMDH based evolutionary algorithms to predict the scour pile under clear-water condition.” Ocean Engineering, Vol. 99, pp. 85–94, DOI:  https://doi.org/10.1016/j.oceaneng.2015.01.014.CrossRefGoogle Scholar
  16. Najafzadeh, M., Barani, G. A., and Hessami-Kermani, M. R. (2015). “Evaluation of GMDH networks for prediction of local scour depth at bridge abutments in coarse sediments with thinly armored beds.” Ocean Engineering, Vol. 104, pp. 387–396, DOI:  https://doi.org/10.1016/j.oceaneng.2015.05.016.CrossRefGoogle Scholar
  17. Najafzadeh, M. and Lim, S. Y. (2015). “Application of improved neuro-fuzzy GMDH to predict scour downstream of sluice gates.” Earth Science Informatics, Vol. 8, No. 1, pp. 187–196, DOI:  https://doi.org/10.1007/s12145-014-0144-8.CrossRefGoogle Scholar
  18. Najafzadeh, M., Saberi-Movahed, F., and Sarkamaryan, S. (2018). “NF-GMDH Systems based evolutionary algorithms for evaluation of bridge pier local scour depth with debris effects.” Marine Georesources & Geotechnology, Vol. 36, No. 5, pp. 589–602.CrossRefGoogle Scholar
  19. Najafzadeh, M. and Tafarojnoruz, A. (2016). “Evaluation of neuro-fuzzy gmdh based particle swarm optimization to predict longitudinal dispersion coefficients in rivers.” Environmental Earth Sciences, Vol. 75, No. 157, pp. 1–16, DOI:  https://doi.org/10.1007/s12665-015-4877-6.Google Scholar
  20. Nayak, P. C., Rao, Y. R. S., and Sudheer, K. P. (2006). “Groundwater level forecasting in a shallow aquifer using artificial neural network approach.” Water Resources Management, Vol. 20, No. 1, pp. 77–90, DOI:  https://doi.org/10.1007/s11269-006-4007-z.CrossRefGoogle Scholar
  21. Noori, R., Karbassi, A. R., Moghaddamnia, A., Han, D., Zokaei-Ashtiani, M. H., Farokhnia, A., and Gousheh, M. G. (2011). “Assessment of input variables determination on the SVM model performance using PCA, gamma test, and forward selection techniques for monthly stream flow prediction.” Journal of Hydrology, Vol. 401, pp. 177–189, DOI:  https://doi.org/10.1016/j.jhydrol.2011.02.021.CrossRefGoogle Scholar
  22. Rashedi, E., Nezamabadi-Pour, H., and Saryazdi, S. (2009). “GSA: A gravitational search algorithm.” Information Sciences, Vol. 179, pp. 2232–2248, DOI:  https://doi.org/10.1016/j.ins.2009.03.004.CrossRefGoogle Scholar
  23. Rezaie-Balf, M., Naggana, S. R., Ghaemi, A., and Deka, P. C. (2017). “Wavelet coupled MARS and M5 model tree approaches for groundwater level forecasting.” Journal of Hydrology, Vol. 553, pp. 356–373, DOI:  https://doi.org/10.1016/j.jhydrol.2017.08.006.CrossRefGoogle Scholar
  24. Rizzo, D. M. and Dougherty, D. E. (1994). “Characterization of aquifer properties using artificial neural networks: Neural kriging.” Water Resources Research, Vol. 30, No. 2, pp. 483–497, DOI:  https://doi.org/10.1029/93WR02477.CrossRefGoogle Scholar
  25. Salem, G. S. A., Kazama, S., Komori, D., Shahid, S., and Dey, N. C. (2017a). “Optimum abstraction of ground water for sustaining groundwater level and reducing irrigation cost.” Water Resources Management, Vol. 31, No. 6, pp. 1947–1959, DOI:  https://doi.org/10.1007/s11269-017-1623-8.CrossRefGoogle Scholar
  26. Salem, G. S. A., Kazama, S., Shahid, S., and Dey, N. C. (2017b). “Impact of temperature changes on groundwater levels and irrigationcosts in a groundwater-dependent agricultural region in Northwest Bangladesh.” Hydrological Research Letters, Vol. 11, No. 1, pp. 85–91, DOI:  https://doi.org/10.3178/hrl.11.85.CrossRefGoogle Scholar
  27. Shaghaghi, S., Bonakdari, H., Gholami, A., Ebtehaj, I., and Zeinolabedini, M. (2017). “Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design.” Applied Mathematics and Computation, Vol. 313, pp. 271–286, DOI:  https://doi.org/10.1016/j.amc.2017.06.012.CrossRefGoogle Scholar
  28. Shiri, J. and Kisi, O. (2011). “Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations.” Computers & Geosciences, Vol. 37, No. 10, pp. 1692–1701, DOI:  https://doi.org/10.1016/j.cageo.2010.11.010.CrossRefGoogle Scholar
  29. Shiri, J., Kisi, O., Yoon, H., Lee, K. K., and Nazemi, A. H. (2013). “Predicting ground water level fluctuations with meteorological effect implications–A comparative study among soft computing techniques.” Computers & Geosciences, Vol. 56, pp. 32–44, DOI:  https://doi.org/10.1016/j.cageo.2013.01.007.CrossRefGoogle Scholar
  30. Suryanarayana, C., Sudheer, C., Mahammood, V., and Panigrahi, B. K. (2014). “An integrated wavelet-support vector machine for groundwaterlevel prediction in Visakhapatnam, India.” Neurocomputing, Vol. 145, pp. 324–335, DOI:  https://doi.org/10.1016/j.neucom.2014.05.026.CrossRefGoogle Scholar
  31. Szidarovszky, F., Coppola, E., Long, J., Hall, A., and Poulton, M. (2007). “A hybrid artificial neural network-numerical model for groundwater problems.” Ground Water, Vol. 45, No. 5, pp. 590–600, DOI:  https://doi.org/10.1111/j.1745-6584.2007.00330.x.CrossRefGoogle Scholar
  32. Yoon, H., Jun, S. C., Hyun, Y., Bae, G. O., and Lee, K. K. (2010). “A comparative study of artificial neural networks and support vector machines for predicting ground-water levels in a coastal aquifer.” Journal of Hydrology, Vol. 396, pp.128–138, DOI:  https://doi.org/10.1016/j.jhydrol.2010.11.002.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers 2019

Authors and Affiliations

  1. 1.Dept. of Water EngineeringFerdowsi University of MashhadMashhadIran

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