Streamflow Duration Assessment Modeling
Description
Improved streamflow classification
Detailed example
Prediction of whether a streamflow is “perennial,” “intermittent,” or “ephemeral”
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Because streamflow duration classes is unknown for most streams across the nation, the classifications from Streamflow Duration Assessment Methods (SDAMs) can inform ecological assessments and resource management decisions, such as setting restoration goals or applying appropriate water quality standards. SDAMs also support identifying waters that may be subject to regulatory jurisdiction under the Clean Water Act or other authorities.
Audit / financial statement impact
The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Most of the data used to train and evaluate performance of the model was collected by EPA or contractors specifically for the development of SDAMs. The data can be found here: - PNW https://www.hydroshare.org/resource/fb4e7b8758d0478cbfe0d6c786f0f968/ - NE and SE https://catalog.data.gov/dataset/nese-betasdam-final-data-and-code - AW https://catalog.data.gov/dataset/aw-betasdam-final-data-and-code - WM https://catalog.data.gov/dataset/wm-betasdam-final-data-and-code - GP https://catalog.data.gov/dataset/gp-betasdam-final-data-and-code