Optimal field-scale groundwater remediation using neural networks and the genetic algorithm

Conference · Sat May 01 00:00:00 EDT 1993

OSTI ID:10192070

Rogers, L L; Dowla, F U; Johnson, V M

We present a new approach for field-scale nonlinear management of groundwater remediation. First, an artificial neural network (ANN) is trained to predict the outcome of a groundwater transport simulation. Then a genetic algorithm (GA) searches through possible pumping realizations, evaluating the fitness of each with a prediction from the trained ANN. Traditional approaches rely on optimization algorithms requiring sequential calls of the groundwater transport simulation. Our approach processes the transport simulations in parallel and ``recycles`` the knowledge base of these simulations, greatly reducing the computational and real-time burden, often the primary impediment to developing field-scale management models. We present results from a Superfund site suggesting that such management techniques can reduce cleanup costs by over a hundred million dollars.

• Research Organization: Lawrence Livermore National Lab., CA (United States)
• Sponsoring Organization: USDOE, Washington, DC (United States)
• DOE Contract Number: W-7405-ENG-48
• OSTI ID: 10192070
• Report Number(s): UCRL-JC-113773; CONF-9306249-1; ON: DE93040499
• Resource Relation: Conference: Computational technology initiative for the oil and gas industry,Los Alamos, NM (United States),20-24 Jun 1993; Other Information: PBD: May 1993
• Country of Publication: United States
• Language: English

Related Subjects

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS
54 ENVIRONMENTAL SCIENCES
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
GROUND WATER
REMEDIAL ACTION
NEURAL NETWORKS
PERFORMANCE
ENVIRONMENTAL TRANSPORT
COST
ALGORITHMS
ORGANIC COMPOUNDS
VOLATILE MATTER
054000
540220
990301
HEALTH AND SAFETY
CHEMICALS MONITORING AND TRANSPORT
DATA HANDLING