Operational Turbidity Forecast Using Both Recurrent and Feed-Forward Based Multilayer Perceptrons

  • Michaël Savary
  • Anne JohannetEmail author
  • Nicolas Massei
  • Jean-Paul Dupont
  • Emmanuel Hauchard
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)


Approximately 25% of the world population drinking water depends on karst aquifers. Nevertheless, due to their poor filtration properties, karst aquifers are very sensitive to pollutant transport and specifically to turbidity. As physical processes involved in solid transport (advection, diffusion, deposit…) are complicated and badly known in underground conditions, a black-box modelling approach using neural networks is promising. Despite the well-known ability of universal approximation of multilayer perceptron, it appears difficult to efficiently take into account hydrological conditions of the basin. Indeed these conditions depend both on the initial state of the basin (schematically wet or dry), and on the intensity of rainfalls. To this end, an original architecture has been proposed in previous works to take into account phenomenon at large temporal scale (moisture state), coupled with small temporal scale variations (rainfall). This architecture, called hereafter as “two-branches” multilayer perceptron is compared with the classical two layers perceptron for both kinds of modelling: recurrent and non-recurrent. Applied in this way to the Yport pumping well (Normandie, France) with 12 h lag time, it appears that both models proved crucial information: amplitude and synchronization are better with “two-branches” feed forward model when thresholds surpassing prediction is better using classical feed forward perceptron.


Neural networks Recurrent Feed-forward Turbidity Karst 



The authors would like to thank the CODAH for providing rainfall and turbidity data. The Normandie Region and Seine-Normandie Water Agency are thanked for the co-funding of the study. We are also very grateful to S. Lemarie and J. Ratiarson of for the very helpful discussions they helped organize. Our thanks are extended to D. Bertin for his extremely fruitful collaboration in the design and implementation of the Neural Network simulation tool: RnfPro.


  1. 1.
    Kisi, O., Dailr, A.H., Cimen, M., Shiri, J.: Suspended sediment modeling using genetic programming and soft computing techniques. J. Hydrol. 450, 48–58 (2012)CrossRefGoogle Scholar
  2. 2.
    Rajaee, T., Mirbagheri, S.A., Zounemat-Kermani, M., Nourani, V.: Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Sci. Total Environ. 407(17), 4916–4927 (2009)CrossRefGoogle Scholar
  3. 3.
    Massei, N., Dupont, J.P., Mahler, B.J., Laignel, B., Fournier, M., Valdes, D., Ogier, S.: Investigating transport properties and turbidity dynamics of a karst aquifer using correlation, spectral, and wavelet analyses. J. Hydrol. 329, 1–2, 244–25 (2006)Google Scholar
  4. 4.
    Nieto, P.G., García-Gonzalo, E., Fernández, J.A., Muñiz, C.D.: Hybrid PSO–SVM-based method for long-term forecasting of turbidity in the Nalón river basin: a case study in Northern Spain. Ecol. Eng. 73, 192–200 (2014)CrossRefGoogle Scholar
  5. 5.
    Iglesias, C., Torres, J.M., Nieto, P.G., Fernández, J.A., Muñiz, C.D., Piñeiro, J.I., Taboada, J.: Turbidity prediction in a river basin by using artificial neural networks: a case study in northern Spain. Water Resour. Manag. 28(2), 319–331 (2014)CrossRefGoogle Scholar
  6. 6.
    Beaudeau, P., Leboulanger, T., Lacroix, M., Hanneton, S., Wang, H.Q.: Forecasting of turbid floods in a coastal, chalk karstic drain using an artificial neural network. Ground Water 39(1), 109–118 (2001)CrossRefGoogle Scholar
  7. 7.
    Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)CrossRefGoogle Scholar
  8. 8.
    Barron, A.R.: Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans. Inf. Theory 39(3), 930–945 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Dreyfus, G.: Neural Networks: Methodology and Applications, p. 497. Springer Science & Business Media (2005)Google Scholar
  10. 10.
    Artigue, G., Johannet, A., Borrell, V., Pistre, S.: Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France). Nat. Hazards Earth Syst. Sci. 12(11), 3307–3324 (2012)CrossRefGoogle Scholar
  11. 11.
    Johannet, A., Vayssade, B., Bertin, D.: Neural networks: from black box towards transparent box. Application to evapotranspiration modeling. Int. J. Comput. Intell. 4(3), 163–170 (2008)Google Scholar
  12. 12.
    Toukourou, M., Johannet, A., Dreyfus, G., Ayral, P.A.: Rainfall-runoff modeling of flash floods in the absence of rainfall forecasts: the case of “Cévenol Flash Floods”. Appl. Intell. 35(2), 178–189 (2011)CrossRefGoogle Scholar
  13. 13.
    Geman, S., Bienenstock, E., Doursat, R.: Neural networks and the bias/variance dilemma. Neural Comput. 4(1), 1–58 (1992)CrossRefGoogle Scholar
  14. 14.
    Stone, M.: Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. Ser. B (Methodological) 111–147 (1974)Google Scholar
  15. 15.
    Kong-A-Siou, L., Johannet, A., Valérie, B.E., Pistre, S.: Optimization of the generalization capability for rainfall–runoff modeling by neural networks: the case of the Lez aquifer (southern France). Environ. Earth Sci. 65(8), 2365–2375 (2012)CrossRefGoogle Scholar
  16. 16.
    Kong-A-Siou, L., Johannet, A., Borrell, V., Pistre, S.: Complexity selection of a neural network model for karst flood forecasting: the case of the Lez basin (southern France). J. Hydrol. 403, 367–380 (2011)CrossRefGoogle Scholar
  17. 17.
    Darras, T., Johannet, A., Vayssade, B., Kong-A-Siou, L., Pistre, S.: In: Garcia, G.R. (eds.) Influence of the Initialization of Multilayer Perceptron for Flash Floods Forecasting: How Designing a Robust Model, (ITISE 2014), pp. 687–698. Ruiz, IR (2014)Google Scholar
  18. 18.
    Nash, J.E., Sutcliffe, J.V.: River flow forecasting through conceptual models part I-A discussion of principles. J. Hydrol. 10(3), 282–290 (1970)CrossRefGoogle Scholar
  19. 19.
    Moussa, R.: When monstrosity can be beautiful while normality can be ugly: assessing the performance of event-based flood models. Hydrol. Sci. J. 55(6) (2010). Special Issue: the court of miracles of hydrology, pp. 1074–1084Google Scholar
  20. 20.
    Kitanidis, P.K., Bras, R.L.: Real-time forecasting with a conceptual hydrologic model: 2 applications and results. Water Resour. Res. 16(6), 1034–1044 (1980)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michaël Savary
    • 1
    • 2
  • Anne Johannet
    • 2
    Email author
  • Nicolas Massei
    • 1
  • Jean-Paul Dupont
    • 1
  • Emmanuel Hauchard
    • 3
  1. 1.M2C LaboratoryRouen UniversityMont-Saint-AignanFrance
  2. 2.LGEIEcole des mines d’AlèsAlès CedexFrance
  3. 3.Communauté d’Agglomération HavraiseLe HavreFrance

Personalised recommendations