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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.)

Abstract

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.

Keywords

Neural networks Recurrent Feed-forward Turbidity Karst 

Notes

Acknowledgements

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.

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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

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