Artificial neural networks approximation of density dependent saltwater intrusion process in coastal aquifers
Journal Publication ResearchOnline@JCUAbstract
The flow and transport processes in a coastal aquifer are highly nonlinear, where both the flow and transport processes become density dependent. Therefore, numerical simulation of the saltwater intrusion process in such an aquifer is complex and time consuming. An approximate simulation of those complex flow and transport processes may be very useful, if sufficiently accurate, especially where repetitive simulations of these processes are necessary. A simulation methodology using a trained artificial neural network (ANN)is developed to approximate the three-dimensional density dependent flow and transport processes in a coastal aquifer. The data required for initially training the ANN model is generated by using a numerical simulation model (FEMWATER). The simulated data consisting of corresponding sets of input and output patterns are used to train a multilayer perceptron using the back-propagation algorithm. The trained ANN predicts the concentration at specified observation locations at different times. The performance of the ANN as a simulator of the density dependent saltwater intrusion process in a coastal aquifer is evaluated using an illustrative study area. These evaluation results show that the ANN technique can be successfully used for approximating the three-dimensional flow and transport processes in coastal aquifers.
Journal
Journal of Hydrologic Engineering
Publication Name
N/A
Volume
12
ISBN/ISSN
1943-5584
Edition
N/A
Issue
3
Pages Count
12
Location
N/A
Publisher
American Society of Civil Engineers
Publisher Url
N/A
Publisher Location
N/A
Publish Date
N/A
Url
N/A
Date
N/A
EISSN
N/A
DOI
10.1061/(ASCE)1084-0699(2007)12:3(273)