Network dissection of neural networks used in optimal groundwater remediation
We have been using an innovative computational approach for optimal groundwater management which involves use of artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict a particular aspect of the outcome of the flow and transport simulation. Then the.GA directs a search, based on the mechanics of genetics and natural selection, through possible management solutions, in this case patterns or realizations of pumping. These pumping realizations are presented to the trained ANN which predicts the outcome of the pumping realizations. The primary advantages of the ANN approach are parallel processing for the flow and transport simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these flow and transport simulations.
- 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:
- 10157750
- Report Number(s):
- UCRL-JC-112428; CONF-930585-4; ON: DE93013849
- Resource Relation:
- Conference: 2. USA/CIS joint conference on environmental hydrology and hydrogeology,Washington, DC (United States),16-29 May 1993; Other Information: PBD: Dec 1992
- Country of Publication:
- United States
- Language:
- English
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Optimal groundwater remediation using artificial neural networks and the genetic algorithm
Optimal groundwater remediation using artificial neural networks and the genetic algorithm
Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
GROUND WATER
REMEDIAL ACTION
ALGORITHMS
MATHEMATICAL MODELS
FLUID FLOW
LAWRENCE LIVERMORE LABORATORY
540250
990200
540220
SITE RESOURCE AND USE STUDIES
MATHEMATICS AND COMPUTERS
CHEMICALS MONITORING AND TRANSPORT