Prediction of water flow depth with kinematic wave equations and NARMAX approach based on neural networks in overland flow model

This paper deals with predict the water flow depth in presence of exceptional rain by two methods and to compare the performances of them based on error measurements. Two approaches are used and then compared: The knowledge modeling based on kinematic wave approach and the NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs) neural networks approach, based on real data taken from the Tondi Kiboro catchment area of Niger by a group from the IGE laboratory. For the first approach, the aim is to minimize the error between the measured and calculated flow rate values and to use the values of the parameters estimated during the optimization problem to calculate the new water flow depth values. For the second approach which is an unconventional method based on neural networks, the attempt is made to estimate the flow rate values using a recursive relation of the NARMAX approach through the use of a supervised learning model.