Linking multi-media modeling with machine learning to assess and predict lake chlorophyll a concentrations

The datasets include hydrological parameters such as streamflow, soil moisture and water temperature, and meteorological data such as precipitation, max and min temperature, evaporation from 2002 to 2017 for Lake Erie.

This dataset is associated with the following publication: Feng Chang, C., V. Cover, C. Tang, P. Vlahos, D. Wanik, J. Yan, J. Bash, and M. Astitha. Linking multi-media modeling with machine learning to assess and predict lake chlorophyll a concentrations. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 47(6): 1656-1670, (2021).

Data and Resources

Field Value
accessLevel public
bureauCode {020:00}
catalog_conformsTo https://project-open-data.cio.gov/v1.1/schema
identifier https://doi.org/10.23719/1524526
license https://pasteur.epa.gov/license/sciencehub-license.html
modified 2022-04-08
programCode {020:000}
publisher U.S. EPA Office of Research and Development (ORD)
publisher_hierarchy U.S. Government > U.S. Environmental Protection Agency > U.S. EPA Office of Research and Development (ORD)
references {https://doi.org/10.1016/j.jglr.2021.09.011,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364922}
resource-type Dataset
source_datajson_identifier true
source_hash 4e30757225887c03ab92c50738f8ec971180b4ee
source_schema_version 1.1
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • AmeriGEO
  • AmeriGEOSS
  • CKAN
  • GEO
  • GEOSS
  • National
  • North America
  • United States
  • air-temperatire
  • precipitation
  • soil-moisture
  • streamflow
  • water-temperature
isopen False
license_id other-license-specified
license_title other-license-specified
maintainer Chunling Tang
maintainer_email tang.chunling@epa.gov
metadata_created 2025-09-24T07:55:28.851902
metadata_modified 2025-09-24T07:55:28.851910
notes The datasets include hydrological parameters such as streamflow, soil moisture and water temperature, and meteorological data such as precipitation, max and min temperature, evaporation from 2002 to 2017 for Lake Erie. This dataset is associated with the following publication: Feng Chang, C., V. Cover, C. Tang, P. Vlahos, D. Wanik, J. Yan, J. Bash, and M. Astitha. Linking multi-media modeling with machine learning to assess and predict lake chlorophyll a concentrations. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 47(6): 1656-1670, (2021).
num_resources 1
num_tags 13
title Linking multi-media modeling with machine learning to assess and predict lake chlorophyll a concentrations