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

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.