A Bayesian network to predict vulnerability to sea-level rise: data report

Data Series 601
By: , and 



During the 21st century, sea-level rise is projected to have a wide range of effects on coastal environments, development, and infrastructure. Consequently, there has been an increased focus on developing modeling or other analytical approaches to evaluate potential impacts to inform coastal management. This report provides the data that were used to develop and evaluate the performance of a Bayesian network designed to predict long-term shoreline change due to sea-level rise. The data include local rates of relative sea-level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline-change rate compiled as part of the U.S. Geological Survey Coastal Vulnerability Index for the U.S. Atlantic coast. In this project, the Bayesian network is used to define relationships among driving forces, geologic constraints, and coastal responses. Using this information, the Bayesian network is used to make probabilistic predictions of shoreline change in response to different future sea-level-rise scenarios.

Study Area

Publication type Report
Publication Subtype USGS Numbered Series
Title A Bayesian network to predict vulnerability to sea-level rise: data report
Series title Data Series
Series number 601
DOI 10.3133/ds601
Year Published 2011
Language English
Publisher U.S. Geological Survey
Publisher location Reston, VA
Contributing office(s) Woods Hole Coastal and Marine Science Center
Description 15 p.; Download of Data Files
First page 1
Last page 15
Country United States
Additional Online Files (Y/N) Y
Google Analytic Metrics Metrics page
Additional publication details