The work presented here aims at developing a flow forecast model dedicated to real-time management. The proposed model is based on the notion of a transfer function for a linear system identified through the Kalman filter algorithm. In a first step, the transfer function model is linked to the Muskingum semi-empirical model; then it is modified to eliminate the autoregressive component. The Kalman filter algorithm allows the parameters of the proposed model to be updated upon the reception of each new measure with respect to the forecast errors observed in real time. To analyze the performance of the proposed model, its results are compared with those obtained using the dynamic wave model and the simplified kinematic wave model. Because of the absence of measured downstream flow values corresponding to the input hydrograph, the results from the dynamic wave model are used as reference values to evaluate the performance of the other models. These results are also used with the addition of noises to simulate measured values and feed, in "real-time," the identification algorithm of the transfer function in order to adjust, a posteriori, its parameters according to its differences in the flow prediction. The results obtained by the transfer function model agree with those obtained by the dynamic model following the three performance criteria employed. The Nash coefficient and the ratio between the peak flows are close to unity in all of the cases. Also, the lag between the peak flows estimated by the two models is negligible.Key words: waste water networks, real-time management, flow propagation models, forecast, transfer function, Kalman filter.[Journal Translation]
Development of Grid-based Dynamic Wave Model
