RAINFALL-RUNOFF MODELING BY NEURAL NETWOR KS AND KALMAN FILTER

River flow results from the interplay of numerous variables for which quantitative information is not easily available. The aim of this pape r was to develop a river-flow forecasting model based only on rainfall and runoff information. The proposed model has been implemented with the combined utilization of two methods: a neural network method, whic h takes into account the non-linearity of the rainfall-runoff relation ship and an adaptative technique, the Kalman filter, allowing real tim e correction of estimates. The rainfall-runoff relationship has been m odelled on two rivers in northern France. The weekly and daily time st ep models gave satisfying forecasts, even for different lead times.