Application of neural networks to waste site screening
Waste site screening requires knowledge of the actual concentrations of hazardous materials and rates of flow around and below the site with time. The present approach consists primarily of drilling boreholes near contaminated sites and chemically analyzing the extracted physical samples and processing the data. This is expensive and time consuming. The feasibility of using neural network techniques to reduce the cost of waste site screening was investigated. Two neural network techniques, gradient descent back propagation and fully recurrent back propagation were utilized. The networks were trained with data received from Westinghouse Hanford Corporation. The results indicate that the network trained with the fully recurrent technique shows satisfactory generalization capability. The predicted results are close to the results obtained from a mathematical flow prediction model. It is possible to develop a new tool to predict the waste plume, thus substantially reducing the number of the bore sites and samplings. There are a variety of applications for this technique in environmental site screening and remediation. One of the obvious applications would be for optimum well siting. A neural network trained from the existing sampling data could be utilized to decide where would be the best position for the next bore site. Other applications are discussed in the report.
- Research Organization:
- EG and G Idaho, Inc., Idaho Falls, ID (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC07-76ID01570
- OSTI ID:
- 10147763
- Report Number(s):
- EGG-WTD-10677; ON: DE93012163; TRN: 93:016336
- Resource Relation:
- Other Information: PBD: Feb 1993
- Country of Publication:
- United States
- Language:
- English
Similar Records
Development of a neural network predictor for wind power
On Transfer Learning Of Neural Networks Using Bi-Fidelity Data For Uncertainty Propagation
Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
PLUMES
EVALUATION
SITE CHARACTERIZATION
NEURAL NETWORKS
SAMPLING
MATHEMATICAL MODELS
SOILS
COMPARATIVE EVALUATIONS
INJECTION WELLS
MOISTURE
HYDRAULIC CONDUCTIVITY
FLOW RATE
POLLUTANTS
WASTE DISPOSAL
052002
990200
WASTE DISPOSAL AND STORAGE
MATHEMATICS AND COMPUTERS