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Title: Optimal groundwater remediation using artificial neural networks and the genetic algorithm

Abstract

An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the trained network searches through realizations or patterns of pumping selected by the GA, predicting the outcome. This approach has advantages of parallel processing of the groundwater simulations and the ability to [open quotes]recycle[close quotes] or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models. The ANN technology, inspired by neurobiological theories of massive interconnection and parallelism, has been applied to a variety of optimization problems. In the ANN groundwater management approach presented here, the behavior of complex groundwater scenarios with spatially-variable transport parameters and multiple contaminant plumes are simulated with 2-D flow and transport codes. An ANN is trained upon a set of examples developed from groundwater simulations. The input of the ANN characterizes themore » different realizations of pumping. The output characterizes the objectives and constraints of the optimization, such as whether regulatory goals have been met, value of cost functions or cleanup time, and mass of contaminant removal. The supervised learning algorithm of backpropagation is used to train the network. The conjugate gradient method and weight-elimination procedures are used to speed convergence and improve performance, respectively. Then a search is made through possible pumping realizations to find optimal realizations.« less

Authors:
Publication Date:
Research Org.:
Stanford Univ., CA (United States)
OSTI Identifier:
5350518
Resource Type:
Miscellaneous
Resource Relation:
Other Information: Thesis (Ph.D.)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; GROUND WATER; REMEDIAL ACTION; NEURAL NETWORKS; PERFORMANCE TESTING; OPTIMIZATION; COMPUTERIZED SIMULATION; HYDROGEN COMPOUNDS; OXYGEN COMPOUNDS; SIMULATION; TESTING; WATER; 540220* - Environment, Terrestrial- Chemicals Monitoring & Transport- (1990-); 990200 - Mathematics & Computers; 540250 - Environment, Terrestrial- Site Resource & Use Studies- (1990-)

Citation Formats

Rogers, L L. Optimal groundwater remediation using artificial neural networks and the genetic algorithm. United States: N. p., 1992. Web.
Rogers, L L. Optimal groundwater remediation using artificial neural networks and the genetic algorithm. United States.
Rogers, L L. Wed . "Optimal groundwater remediation using artificial neural networks and the genetic algorithm". United States.
@article{osti_5350518,
title = {Optimal groundwater remediation using artificial neural networks and the genetic algorithm},
author = {Rogers, L L},
abstractNote = {An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the trained network searches through realizations or patterns of pumping selected by the GA, predicting the outcome. This approach has advantages of parallel processing of the groundwater simulations and the ability to [open quotes]recycle[close quotes] or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models. The ANN technology, inspired by neurobiological theories of massive interconnection and parallelism, has been applied to a variety of optimization problems. In the ANN groundwater management approach presented here, the behavior of complex groundwater scenarios with spatially-variable transport parameters and multiple contaminant plumes are simulated with 2-D flow and transport codes. An ANN is trained upon a set of examples developed from groundwater simulations. The input of the ANN characterizes the different realizations of pumping. The output characterizes the objectives and constraints of the optimization, such as whether regulatory goals have been met, value of cost functions or cleanup time, and mass of contaminant removal. The supervised learning algorithm of backpropagation is used to train the network. The conjugate gradient method and weight-elimination procedures are used to speed convergence and improve performance, respectively. Then a search is made through possible pumping realizations to find optimal realizations.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1992},
month = {1}
}

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