Towards a neural networks-based prediction tool devoted to low water-levels forecasting. Case study on the Méjannes-le-Clap karst aquifer (France)

Abstract : Karst aquifers provide water resource for a large part of the Mediterranean population and water resource becomes a strategic problem during summer when population increases due to tourism. To help managers to optimize the exploitation of water, this paper studies the ability of a neural network model to efficiently simulate water levels in the Cèze River, connected to a karst aquifer, few months ahead during the dry season. The neural model is a recurrent multilayer perceptron that learns the relations between inputs (mainly rainfall and ETP) and output (water level). After a training step using 17 years of data, the model is assessed on a never seen year to be validated. A particular attention is devoted to the dry season (from May to September). The model achieved good forecast of the maximal observed drawdown. Several architectures were run, each one related to a specific hypothesis about the behaviour of the hydrosystem or about the strategy of modelling. Recurrent multilayer perceptrons were used to achieve these tasks and thanks a rigorous process to variables and model complexity selection, obtained performances were very satisfying. Assessment of the model was done on one of the drier summer of the database composed of 19 years of daily water-levels. For the five tested architectures, Nash criteria evolve between 0.84 to 0.9 on the whole year, and between 0 to 0.6 on the drier period (June to August). As this modelling strategy can be fed by rainfall scenario, this kind of models could help managers to optimize anthropogenic impacts on the river to preserve natural ecosystems. The methodology is generic and can thus be used with profit by managers on other hydrosystems.
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https://hal.umontpellier.fr/hal-02127821
Contributeur : Pascale Roussel <>
Soumis le : lundi 13 mai 2019 - 17:18:13
Dernière modification le : vendredi 14 juin 2019 - 02:06:04

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  • HAL Id : hal-02127821, version 1

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Adrien Coutouis, A. Johannet, Séverin Pistre, P.A. Ayral, Laurent Cadilhac. Towards a neural networks-based prediction tool devoted to low water-levels forecasting. Case study on the Méjannes-le-Clap karst aquifer (France). 8th International Congress on Environmental Modelling and Software (iEMSs), Jul 2016, Toulouse, France. ⟨hal-02127821⟩

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