HR: 1340h
AN: H53F-0541    [Abstracts]
TI: Efficiency of ANN Model in Approximating Saltwater Intrusion Process in Coastal Aquifers with Noisy Data
AU: * Bhattacharjya, R K
EM: rajibkb@nits.ac.in
AF: Department of Civil Engineering, National Institute of Technology Silchar, Silchar, AS 788010 India
AB: The over exploitation of groundwater resources has become unavoidable in many parts of the world due to the increasing demands for freshwater. The unplanned exploitation of freshwater from coastal aquifers may cause saltwater intrusion into coastal aquifers. The flow and transport equations are to be solved in order to model saltwater intrusion process in coastal aquifers. The density variations in transition zone make the flow and transport equations highly complex and nonlinear. Therefore, simulation of these highly nonlinear processes is also complex and costly in terms of computer memory and computational time requirements. An approximate simulation of these complex processes would be useful especially when repetitive simulations are necessary. There are various possible applications for such an approximator, if the simulations are reasonably accurate. One possible application would be the use of such an approximator for simulation, linked to an optimization model for decision making. This paper evaluates the efficiency of an artificial neural networks model (ANN) in approximating three dimensional density depended saltwater intrusion process in coastal aquifer and also evaluates its performance with noisy data. The data required for training and testing the ANN model is generated using a numerical simulation model. The trained ANN predicts the concentration at specified observation locations at different times. The performance of the ANN model is evaluated using an illustrative study area. These evaluation results show that the performance of the ANN model is quite satisfactory and does not degrade much with noisy data. For example, the average absolute relative error (AARE) value during testing is 4.86 when the training patters are free form error. The AARE value increases to 5.674 when the training patterns are perturbed with normally distributed error of standard deviation (SD) 0.05, and mean zero. Similarly, the AARE value increases up to 6.701, 7.489 and 9.194 when the training patterns are perturbed with normally distributed error of SD 0.10, 0.15, and 0.20 respectively and mean zero. The performance of the model is also evaluated with respect to threshold statistics (TSx) for an absolute relative error (ARE). The ANN model is very sensitive with respective to TS5 statistics. The average value of TS5 is 70.910% when the ANN model is trained with error free patters. This indicates that 70.910% of predicted concentration values had an ARE value less than 5%. The TS5 value decreases to 64.518% when the training patterns are perturbed with normally distributed error of SD 0.05 and mean zero. The TS5 values further decrease to 56.426%, 50.301%, and 38.883% when the training patterns are perturbed with normally distributed error of SD 0.10, 0.15, 0.20 respectively and mean zero. The ANN model is less sensitive for TS40 and TS50 statistics. For example the TS40 value is 99.693% when the ANN model is trained with error free patterns and 98.816% when the ANN model is trained with the patterns perturbed with normally distributed error of SD 0.20 and mean zero. The performance evaluations of the developed ANN model show that this approach is potentially useful for approximate simulation of the transient density dependent three dimensional flow and transport processes in coastal aquifers. The performance of the model does not degrade much with noisy data. However, more rigorous evaluations using larger and more complex study areas are necessary before the applicability of this approach can be fully established.
DE: 1828 Groundwater hydraulics
DE: 1829 Groundwater hydrology
DE: 1831 Groundwater quality
DE: 1832 Groundwater transport
SC: Hydrology [H]
MN: Fall Meeting 2005