Estuarine Back-barrier Shoreline and Beach Sandline Change Model Skill and Predicted Probabilities: Event-driven backshore shoreline change

The Barrier Island and Estuarine Wetland Physical Change Assessment was created to calibrate and test probability models of barrier island estuarine shoreline (backshore) and beach sandline change for study areas in Virginia, Maryland, and New Jersey. The models examined the influence of hydrologic and physical variables related to long-term and storm-derived overwash and back-barrier shoreline change. Input variables were constructed into a Bayesian Network (BN) using Netica, a computer program created by NORSYS Software Corporation that allows users to work with belief networks and influence diagrams. Each model is tested on its ability to predict changes in long-term and event-driven (for example, Hurricane Sandy-induced) backshore and sandline change based on learned correlations from the input variables across the domain. Using the input hydrodynamic and geomorphic data, the BN is constrained to produce a prediction of an updated conditional probability of backshore or sandline change at each location. To evaluate the ability of the BN to reproduce the observations used to train the model, the skill, log likelihood ratio and probability predictions were utilized. These data are the probability and skill metrics for the event-driven estuarine back-barrier shoreline change model.

Data e Risorse

Campo Valore
accessLevel public
bureauCode {010:12}
catalog_@context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
catalog_conformsTo https://project-open-data.cio.gov/v1.1/schema
catalog_describedBy https://project-open-data.cio.gov/v1.1/schema/catalog.json
datagov_dedupe_retained 20220725164314
identifier USGS:7604e220-dba1-484d-815f-d41b3c540560
metadata_type geospatial
modified 20201013
old-spatial {"type": "Polygon", "coordinates": [[[-75.382739, 37.862809], [-75.382739, 40.479022], [ -73.974687, 40.479022], [ -73.974687, 37.862809], [-75.382739, 37.862809]]]}
publisher U.S. Geological Survey
publisher_hierarchy Department of the Interior > U.S. Geological Survey
resource-type Dataset
source_datajson_identifier true
source_hash 04dd26491b58f2b8f069cd606815e93d5c04b7bf
source_schema_version 1.1
spatial {"type": "Polygon", "coordinates": [[[-75.382739, 37.862809], [-75.382739, 40.479022], [ -73.974687, 40.479022], [ -73.974687, 37.862809], [-75.382739, 37.862809]]]}
theme {geospatial}
Gruppi
  • AmeriGEOSS
  • National Provider
  • North America
Tag
  • amerigeo
  • amerigeoss
  • assateague-island
  • barrier-islands
  • bayesian-models
  • ckan
  • coastal
  • ecology
  • environment
  • geo
  • geology
  • geomorphology
  • geoscientificinformation
  • geoss
  • hurricanes
  • long-term-shoreline-change
  • maryland
  • md
  • mid-atlantic-ocean
  • national
  • new-jersey
  • nj
  • north-america
  • oceans
  • storm-driven-shoreline-change
  • storms
  • united-states
  • usa
  • usgs-7604e220-dba1-484d-815f-d41b3c540560
  • va
  • virginia
isopen False
license_id notspecified
license_title License not specified
maintainer Kathryn E.L. Smith
maintainer_email kelsmith@usgs.gov
metadata_created 2025-11-20T07:35:13.414048
metadata_modified 2025-11-20T07:35:13.414052
notes The Barrier Island and Estuarine Wetland Physical Change Assessment was created to calibrate and test probability models of barrier island estuarine shoreline (backshore) and beach sandline change for study areas in Virginia, Maryland, and New Jersey. The models examined the influence of hydrologic and physical variables related to long-term and storm-derived overwash and back-barrier shoreline change. Input variables were constructed into a Bayesian Network (BN) using Netica, a computer program created by NORSYS Software Corporation that allows users to work with belief networks and influence diagrams. Each model is tested on its ability to predict changes in long-term and event-driven (for example, Hurricane Sandy-induced) backshore and sandline change based on learned correlations from the input variables across the domain. Using the input hydrodynamic and geomorphic data, the BN is constrained to produce a prediction of an updated conditional probability of backshore or sandline change at each location. To evaluate the ability of the BN to reproduce the observations used to train the model, the skill, log likelihood ratio and probability predictions were utilized. These data are the probability and skill metrics for the event-driven estuarine back-barrier shoreline change model.
num_resources 2
num_tags 31
title Estuarine Back-barrier Shoreline and Beach Sandline Change Model Skill and Predicted Probabilities: Event-driven backshore shoreline change