|
Detailed Reference Information |
Coulibaly, P., Anctil, F., Aravena, R. and Bobée, B. (2001). Artificial neural network modeling of water table depth fluctuations. Water Resources Research 37: doi: 10.1029/2000WR900368. issn: 0043-1397. |
|
Three types of functionally different artificial neural network (ANN) models are calibrated using a relatively short length of groundwater level records and related hydrometeorlogical data to simulate water table fluctuations in the Gondo aquifer, Burkina Faso. Input delay neural network (IDNN) with 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 compared with results obtained from two variants of radial basis function (RBF) networks, namely, a generalized RBF model (GRBF) and a probabilistic neural network (PNN). Overall, simulation results suggest that the RNN is the most 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 poorly as compared to the other models. Furthermore, the study shows that RNN may offer a robust framework for improving water supply planning in semiarid areas where aquifer information is not available. This study has significant implications for groundwater management in areas with inadequate groundwater monitoring network. ¿ 2001 American Geophysical Union |
|
|
|
BACKGROUND DATA FILES |
|
|
Abstract |
|
|
|
|
|
Keywords
Hydrology, Groundwater hydrology |
|
Publisher
American Geophysical Union 2000 Florida Avenue N.W. Washington, D.C. 20009-1277 USA 1-202-462-6900 1-202-328-0566 service@agu.org |
|
|
|