Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling
- Lawrence Livermore National Lab., CA (United States)
A new approach to nonlinear groundwater management methodology is presented which optimizes aquifer remediation with the aid of artificial neural networks (ANNs). The methodology allows solute transport simulations, usually the main computational component of management models, to be run in parallel. The ANN technology, inspired by neurobiological theories of massive interconnection and parallelism, has been successfully applied to a variety of optimization problems. In this new approach, optimal management solutions are found by (1) first training an ANN to predict the outcome of the flow and transport code, and (2) then using the trained ANN to search through many pumping realizations to find an optimal one for successful remediation. The behavior of complex groundwater scenarios with spatially variable transport parameters and multiple contaminant plumes is simulated with a two-dimensional hybrid finite-difference/finite-element flow and transport code. The flow and transport code develops the set of examples upon which the network is trained. The input of the ANN characterizes the different realizations of pumping, with each input indicating the pumping level of a well. The output is capable of characterizing the objectives and constraints of the optimization, such as attainment of regulatory goals, value of cost functions and cleanup time, and mass of contaminant removal. The supervised learning algorithm of back propagation was used to train the network. The conjugate gradient method and weight elimination procedures are used to speed convergence and improve performance, respectively. Once trained, the ANN begins a search through various realizations of pumping patterns to determine whether or not they will be successful. The search is directed by a simple genetic algorithm. The resulting management solutions are consistent with those resulting from a more conventional optimization technique. 84 refs., 9 figs., 5 tabs.
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 7161471
- Journal Information:
- Water Resources Research; (United States), Vol. 30:2; ISSN 0043-1397
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ENVIRONMENTAL TRANSPORT
PARALLEL PROCESSING
GROUND WATER
FLUID FLOW
REMEDIAL ACTION
OPTIMIZATION
SOLUTES
NEURAL NETWORKS
NONLINEAR PROGRAMMING
HYDROGEN COMPOUNDS
MASS TRANSFER
OXYGEN COMPOUNDS
PROGRAMMING
WATER
540220* - Environment
Terrestrial- Chemicals Monitoring & Transport- (1990-)
990200 - Mathematics & Computers