An artificial neural network based groundwater flow and transport simulator
Artificial neural networks are investigated as a tool for the simulation of contaminant loss and recovery in three-dimensional heterogeneous groundwater flow and contaminant transport modeling. These methods have useful applications in expert system development, knowledge base development and optimization of groundwater pollution remediation. The numerical model runs used to develop the artificial neural networks can be re-used to develop artificial neural networks to address alternative optimization problems or changed formulations of the constraints and or objective function under optimization. Artificial neural networks have been analyzed with the goal of estimating objectives which normally require the use of traditional flow and transport codes: such as contaminant recovery, contaminant loss (unrecovered) and remediation failure. The inputs to the artificial neutral networks are variable pumping withdrawal rates at fairly unconstrained 3-D locations. A forward-feed backwards error propagation artificial neural network architecture is used. The significance of the size of the training set, network architecture, and network weight optimization algorithm with respect to the estimation accuracy and objective are shown to be important. Finally, the quality of the weight optimization is studied via cross-validation techniques. This is demonstrated to be a useful method for judging training performance for strongly under-described systems.
- Research Organization:
- ELSAMPROJEKT A/S, Fredericia (DK)
- OSTI ID:
- 20014942
- Country of Publication:
- United States
- Language:
- English
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