Integration of neural networks in a geographical information system for the monitoring of a catchment area

Frédérik Thiéry (1) , Stéphane Grieu (1) , Adama Traoré (1) , Mathieu Barreau (1) , Monique Polit (1)

1 LP2A - Laboratoire de Physique Appliquée et d'Automatique

The present work takes part in a global development of reliable and robust tools allowing real-time controlling and supervising of the Têt catchment area, the main river of the Pyrénées-Orientales department (Southern France). The impact of the Têt on the department life is significant and the management of its water quality must be largely improved and better supervised. The main purpose of the work was to develop "rain flow" predictive models, using Elman recurrent neural networks and based on the identification of localized rain events. These neural models allow understanding the dynamic evolution, according to rain events, of the Têt flow at a selected point and of the Perpignan WWTP (WasteWater Treatment Plant) influent flow. Their most interesting characteristic is their capability to predict big increases in river flow and in plant influent flow. The neural models have been integrated as thematic layers in a Geographical Information System (G.I.S.) allowing an efficient management and update of the records used to develop the models.

Keywords: Elman neural network; "rain flow" predictive model; monitoring; Geographical Information System

Domains: Automatic

Frédérik Thiéry, Stéphane Grieu, Adama Traoré, Mathieu Barreau, Monique Polit. Integration of neural networks in a geographical information system for the monitoring of a catchment area. Mathematics and Computers in Simulation, 2008, 76 (5-6), pp.388-397. ⟨10.1016/j.matcom.2007.04.011⟩. ⟨hal-01273225⟩