Skip to main content

Advertisement

SpringerLink
Log in

Logical genetic programming (LGP) application to water resources management

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Genetic programming (GP) is a variant of evolutionary algorithms (EA). EAs are general-purpose search algorithms. Yet, GP does not solve multi-conditional problems satisfactorily. This study improves the GP’s predictive skill by development and integration of mathematical logical operators and functions to it. The proposed improvement is herein named logical genetic programming (LGP) whose performance is compared with that of GP using examples from the fields of mathematics and water resources. The results of the examples show the LGP’s superior performance in both examples, with LGP producing improvements of 74 and 42% in the objective functions of the mathematical and water resources examples, respectively, when compared with the GP’s results. The objective functions minimize the mean absolute error (MAE). The comparison of the LGP and GP results with alternative performance criteria demonstrate a better capability of the former algorithm in solving multi-conditional problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Ashofteh, P.-S., Bozorg-Haddad, O., & Mariño, M. A. (2013a). Climate change impact on reservoir performance indices in agricultural water supply. Journal of Irrigation and Drainage Engineering, 139(2), 85–97.

    Article  Google Scholar 

  • Ashofteh, P.-S., Bozorg-Haddad, O., & Mariño, M. A. (2013b). Scenario assessment of streamflow simulation and its transition probability in future periods under climate change. Water Resources Management, 27(1), 255–274.

    Article  Google Scholar 

  • Ashofteh, P.-S., Bozorg-Haddad, O., Akbari-Alashti, H., and Mariño, M. A., (2015). “Determination of irrigation allocation policy under climate change by genetic programming”, Journal of Irrigation and Drainage Engineering (ASCE), 141(4), Doi: https://doi.org/10.1061/(ASCE)IR.1943-4774.0000807, 141 (4), 04014059.

    Article  Google Scholar 

  • Babovic, V., & Keijzer, M. (2002). Rainfall runoff modeling based on genetic programming. Nordic Hydrology, 33(5), 331–346.

    Article  Google Scholar 

  • Bozorg-Haddad, O., Afshar, A., & Mariño, M. A. (2006). Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resources Management, 20(5), 661–680.

    Article  Google Scholar 

  • Bozorg-Haddad, O., Solgi, M., & Loáiciga, H. A. (2017). Meta-heuristic and evolutionary algorithms for engineering optimization. Hoboken: John Wiley & Sons.

    Book  Google Scholar 

  • Cancelliere, A., Ancarani, A., & Rossi, G. (1998). Susceptibility of water supply reservoirs to drought conditions. Journal of Hydrologic Engineering, 3(2), 140–148.

    Article  Google Scholar 

  • Danandeh Mehr, A., Nourani, V., Kahya, E., Hrnjica, B., Sattar, A. M. A., & Mundher Yaseen, Z. (2018). Genetic programming in water resources engineering: a state-of-the-art review. Journal of Hydrology, 566, 643–667. https://doi.org/10.1016/j.jhydrol.2018.09.043.

    Article  Google Scholar 

  • Fallah-Mehdipour, E., Bozorg-Haddad, O., & Marino, M. A. (2013). Prediction and simulation of monthly groundwater levels by genetic programming. Journal of Hydro-Environment Research, 7(4), 253–260.

    Article  Google Scholar 

  • Fernandez, T., & Evett, M. (1998). Numeric mutation as an improvement to symbolic regression in genetic programming. Evolutionary Programming VII, Lecture Notes in Computer Science, Springer Verlag KG, 1447, 251–260.

    Article  Google Scholar 

  • Giustolisi, O. (2004). Using genetic programming to determine Chèzy resistance coefficient in corrugated channels. Journal of Hydroinformatics, 6(3), 157–173.

    Article  Google Scholar 

  • Golubski, W. (2002). New results on fuzzy regression by using genetic programming. Genetic Programming, Lecture Notes in Computer Science, Kinsale, Ireland, 2278, 308–315.

    Article  Google Scholar 

  • Havlíček, V., Hanel, M., Máca, P., Kuráž, M., & Pech, P. (2013). Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting. Computing, 95(1), 363–380.

    Article  Google Scholar 

  • Hu, T. S., Lam, K. C., & Ng, S. T. (2001). River flow time series prediction with a range dependent neural network. Hydrological Sciences Journal, 46(5), 729–745.

    Article  Google Scholar 

  • Langdon, W. B. and Chen, T. (2018). “Genetic programming bibliography”, <http://www.cs.bham.ac.uk/~wbl/biblio/>.

  • Li, W. K., Wang, W. L., & Li, L. (2018). Optimization of water resources utilization by multi-objective moth-flame algorithm. Water Resources Management, 32(10), 3303–3316.

    Article  Google Scholar 

  • Liong, S.-Y., Gautam, T. R., Khu, S. T., Babovic, V., Keijzer, M., & Muttil, N. (2007). Genetic programming: a new paradigm in rainfall runoff modeling. Journal of the American Water Resources Association, 38(3), 705–718.

    Article  Google Scholar 

  • Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection (p. 819). Cambridge, Massachusets, London: MIT Press.

    Google Scholar 

  • Kramer, M. D. and Zhang, D. (2000). “GAPS: a genetic programming system”, The Twenty-Fourth Annual International Computer Software and Applications Conference, Taipei, 25-27 October, pp. 614-619.

  • Loáiciga, H. A. (2002). Reservoir design and operation with variable lake hydrology. Journal of Water Resources Planning and Management, 128(6), 399–405.

    Article  Google Scholar 

  • Loucks, D. P., Stedinger, J. R., & Haith, D. A. (1981). Water resources systems planning and analysis (p. 559). N. J., Prentice-Hall: Englewood Cliffs.

    Google Scholar 

  • Moghadam, S. H., Ashofteh, P.-S., & Loáiciga, H. A. (2019). Application of climate projections and Monte Carlo approach for the assessment of future river flow: case study of the Khorramabad River basin, Iran. Journal of Hydrologic Engineering, 24(7), 05019014. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001801.

    Article  Google Scholar 

  • Morales, C. O. and Vázquez, K. R. (2004). “Symbolic regression problems by genetic programming with multi-branches”, advances in artificial intelligence, lecture notes in computer science, Springer-Verlag, Mexico City, Mexico, 26–30 April, 2972, 717–726.

  • Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900.

    Article  Google Scholar 

  • Development Core Team, R. (2011). R: a language and environment for statistical computing. Vienna. http://www.R-project.org: R Foundation for Statistical Computing.

  • Raman, H., & Chandramouli, V. (1996). Deriving a general operating policy for reservoirs using neural network. Journal of Water Resources Planning and Management, 122(5), 342–347.

    Article  Google Scholar 

  • Savic, D. A., Walters, G. A., & Davidson, J. W. (1999). A genetic programming approach to rainfall-runoff modelling. Water Resources Management, 13(3), 219–231.

    Article  Google Scholar 

  • Searson, D. P. Leahy, D. E., and Willis, M. J. (2011). “Predicting the toxicity of chemical compounds using GPTIPS: a free genetic programming toolbox for MATLAB”, Intelligent Control and Computer Engineering, Lecture Notes in Electrical Engineering, Springer, 70, 83–93.

  • Sheng-Wu, X., & Wei-Wu, W. (2003). Point-tree structure genetic programming method for discontinuous function’s regression. Wuhan University Journal of Natural Sciences, 8(1), 323–326.

    Article  Google Scholar 

  • Shokri A., Bozorg-Haddad O., Mariño M. A. (2014). “Multi-objective quantity–quality reservoir operation in sudden pollution”, Water Resources Management, 28(2):567–586, DOI: https://doi.org/10.1007/s11269-013-0504-z.

    Article  Google Scholar 

  • Silva, S. (2007). GPLAB: a genetic programming toolbox for Matlab, version 3 (pp. 13–15). ECOS-Evolutionary and Complex Systems Group: University of Coimbra, Portugal.

    Google Scholar 

  • Solgi, M., Bozorg-Haddad, O., & Loáiciga, H. A. (2017). The enhanced honey bee mating optimization algorithm for water resources optimization. Water Resources Management, 31(3).

Download references

Acknowledgements

The authors thank Iran’s National Science Foundation (INSF) for its financial support on this research.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, University of Qom, Qom, Iran

    Parisa-Sadat Ashofteh

  2. Department of Irrigation & Reclamation, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran

    Omid Bozorg-Haddad

  3. Department of Geography, University of California, Santa Barbara, CA, 93106, USA

    Hugo A. Loáiciga

Authors
  1. Parisa-Sadat Ashofteh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Omid Bozorg-Haddad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hugo A. Loáiciga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omid Bozorg-Haddad.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ashofteh, PS., Bozorg-Haddad, O. & Loáiciga, H.A. Logical genetic programming (LGP) application to water resources management. Environ Monit Assess 192, 34 (2020). https://doi.org/10.1007/s10661-019-8014-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-019-8014-y

Keywords

Search

Navigation