ASCE will be sunsetting the Civil Engineering Database (CEDB) in the coming months. We encourage you to start using the ASCE Library to perform content searches. In the ASCE Library, you can save your search results as well as receive email content alerts when new content relevant to your area of interest is published. Try it out today at https://ascelibrary.org.

 

Site Characterization by Artificial Neural Networks

by Donna M. Rizzo, Univ of Vermont, Burlington, United States,
David E. Dougherty, Univ of Vermont, Burlington, United States,
Theodore P. Lillys, Univ of Vermont, Burlington, United States,



Document Type: Proceeding Paper

Part of: Water Policy and Management: Solving the Problems

Abstract:

Recently, an optimal groundwater management model has been developed to treat groundwater remediation problems at Lawrence Livermore National Laboratory (LLNL). The objective of the model is to identify the best remediation strategies (well site selection and pumping rates) so that water quality standards are met at a specified reliability level within a given time frame. A thorough understanding of the hydrodynamic behavior of aquifer systems requires a complete and accurate determination of the physical parameters of the groundwater system. An example, the one we will examine here, is the identification of three-dimensional hydraulic conductivity fields for LLNL's Main Site.

Subject Headings: Groundwater management | Neural networks | Water quality | Site investigation | Wells (water) | Water treatment | Standards and codes

Services: Buy this book/Buy this article

 

Return to search