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Modelling non Measurable Processes by Neural Networks: Forecasting Underground Flow Case Study of the Céze Basin (Gard - France)

A. Johannet 1 P.A. Ayral 1 B. Vayssade 2
1 LGEI - Laboratoire de Génie de l'Environnement Industriel
2 IMT - MINES ALES - IMT - MINES ALES
Abstract : After a presentation of the nonlinear properties of neural networks, their applications to hydrology are described. A neural predictor is satisfactorily used to estimate a flood peak. The main contribution of the paper concerns an original method for visualising a hidden underground flow Satisfactory experimental results were obtained that fitted well with the knowledge of local hydrogeology, opening up an interesting avenue for modelling using neural networks.
Keywords : Function Artificial Neural Network Hexapod Robot Multilayer Network Goal Function Neural Network
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https://hal.mines-ales.fr/hal-02927928
Contributor : Administrateur Imt - Mines Alès <>
Submitted on : Wednesday, September 2, 2020 - 10:17:03 AM
Last modification on : Friday, September 4, 2020 - 3:08:15 AM

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A. Johannet, P.A. Ayral, B. Vayssade. Modelling non Measurable Processes by Neural Networks: Forecasting Underground Flow Case Study of the Céze Basin (Gard - France). Advances and Innovations in Systems, Computing Sciences and Software Engineering, Springer Netherlands, pp.53-58, 2007, 978-1-4020-6264-3. ⟨10.1007/978-1-4020-6264-3_10⟩. ⟨hal-02927928⟩

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