Artificial neural network modeling of water table depth fluctuations

Three types of functionally different artificial neural network (ANN) model s are calibrated using a relatively short length of groundwater level recor ds and related hydrometeorological data to simulate water table fluctuation s in the Gondo aquifer, Burkina Faso, Input delay neural network (IDNN) wit h static memory structure and globally recurrent neural network (RNN) with inherent dynamical memory are proposed for monthly water table fluctuations modeling. The simulation performance of the IDNN and the RNN models is com pared with results obtained from two variants of radial basis function (RBF ) networks, namely, a generalized RBF model (GRBF) and a probabilistic neur al network (PNN). Overall, simulation results suggest that the RNN is the m ost efficient of the ANN models tested for a calibration period as short as 7 years. The results of the IDNN and the PNN are almost equivalent despite their basically different learning procedures. The GRBF performs very poor ly as compared to the other models. Furthermore, the study shows that RNN m ay offer a robust framework for improving water supply planning in semiarid areas where aquifer information is not available. This study has significa nt implications for groundwater management in areas with inadequate groundw ater monitoring network.