Experimental Evaluation of Straight Line Programs for Hydrological Modelling with Exogenous Variables

  • Ramón Rueda DelgadoEmail author
  • Luis G. Baca Ruiz
  • Patricia Jimeno-Sáez
  • Manuel Pegalajar Cuellar
  • David Pulido-Velazquez
  • Mara Del Carmen Pegalajar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10334)


The estimation of the future streamflows is one of the main research topics in hydrology and a very important task for water resources management. The aim of this work is to use symbolic regression in order to model the hydrological balance. Specifically, we use genetic programming to solve the symbolic regression problem. Nevertheless, in this work we use Straight Line Programs instead of trees to encode algebraic expression. Results shows that this representation for algebraic expressions could improve the results in both accuracy and computational time.


Genetic programming Straight line programs Symbolic regression Modeling hydrological balance 



This work has been supported by the project TIN201564776-C3-1-R.


  1. 1.
    Alberto, M.R.: Programación genética: La regresión simbólica. Entramado 3, 76–85 (2007)Google Scholar
  2. 2.
    Alonso, C.L., Montaña, J.L., Puente, J., Borges, C.E.: A new linear genetic programming approach based on straight line programs: some theoretical and experimental aspects. Int. J. Artif. Intell. Tools 18(5), 757–781 (2009). CrossRefGoogle Scholar
  3. 3.
    Arnold, J.G., Srinivasan, R., Muttiah, R.S., Williams, J.R.: Large area hydrologic modeling and assesment part i: model development. J. Am. Water Resour. Assoc. 34(1), 73–89 (1998)CrossRefGoogle Scholar
  4. 4.
    Awchi, T.A.: River discharges forecasting in northern Iraq using different ANN techniques. Water Resour. Manage. 28(3), 801–814 (2014)CrossRefGoogle Scholar
  5. 5.
    Benz, F., Kötzing, T.: An effective heuristic for the smallest grammar problem. In: Proceedings of GECCO (Genetic and Evolutionary Computation), pp. 487–494 (2013)Google Scholar
  6. 6.
    Burnash, R.J.C., Ferral, R., McGuire, R.A.: A generalized streamflow simulation system-conceptual modeling for digital computers. National Weather Service and California Department of Water Resources (1973)Google Scholar
  7. 7.
    Chen, Q., Xue, B., Zhang, M.: Generalisation and domain adaptation in GP with gradient descent for symbolic regression. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1137–1144, May 2015Google Scholar
  8. 8.
    Giusti, M., Heintz, J., Morais, J., Morgenstem, J., Pardo, L.: Straight-line programs in geometric elimination theory. J. Pure Appl. Algebra 124(1), 101–146 (1998). CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Huang, W.C., Yang, F.T.: Streamflow estimation using kriging. Water Resour. Res. 34(6), 1599–1608 (1998)CrossRefGoogle Scholar
  10. 10.
    Icke, I., Bongard, J.C.: Improving genetic programming based symbolic regression using deterministic machine learning. In: 2013 IEEE Congress on Evolutionary Computation, pp. 1763–1770, June 2013Google Scholar
  11. 11.
    Krick, T.: Straight-line programs in polynomial equation solving (2002)Google Scholar
  12. 12.
    Langdon, W.B.: Genetic Programming — Computers Using “Natural Selection” to Generate Programs, pp. 9–42. Springer, Boston (1998)Google Scholar
  13. 13.
    Lindstrom, G., Johannson, B., Persson, M., Gardelin, M., Bergstrom, S.: Development and test of the distributed HBV-96 hydrological model. J. Hydrol. 201, 272–288 (1997)CrossRefGoogle Scholar
  14. 14.
    Maarten, K.: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling, pp. 70–82. Springer, Berlin (2003)zbMATHGoogle Scholar
  15. 15.
    McKay, B., Willis, M.J., Barton, G.W.: Using a tree structured genetic algorithm to perform symbolic regression, pp. 487–492, September 1995Google Scholar
  16. 16.
    Oliver Morales, C., Rodríguez Vázquez, K.: Symbolic Regression Problems by Genetic Programming with Multi-branches. Springer, Heidelberg (2004). CrossRefGoogle Scholar
  17. 17.
    Pospíchal, J., Varga, Ľ., Kvasnička, V.: Symbolic Regression of Boolean Functions by Genetic Programming. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  18. 18.
    Sequera, J., del Castillo Diez, J., Sotos, L.: Document clustering with evolutionary systems through straight-line programs slp. Intell. Learn. Syst. Appl. 4(4), 303–318 (2012)Google Scholar
  19. 19.
    Temez, J.R.: Modelo matemático de transformación precipitación-aportación. ASINEL (1977)Google Scholar
  20. 20.
    Tosun, N., Özler, L.: A study of tool life in hot machining using artificial neural networks and regression analysis method. J. Mater. Process. Technol. 124(12), 99–104 (2002). CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ramón Rueda Delgado
    • 1
    Email author
  • Luis G. Baca Ruiz
    • 1
  • Patricia Jimeno-Sáez
    • 2
  • Manuel Pegalajar Cuellar
    • 1
  • David Pulido-Velazquez
    • 3
  • Mara Del Carmen Pegalajar
    • 1
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  2. 2.Department of Civil EngineeringCatholic University of San AntonioMurciaSpain
  3. 3.Instituto Geológico y Minero de EspañaGranadaSpain

Personalised recommendations