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Connecting the dots between state water quality goals and citizen science data
Mind the Gap is an on-line data visualization tool created to help easily spot temporal and geospatial data gaps in water quality or benthic sampling collection across the Chesapeake Bay watershed.
How does a tributary of the South River compare with the Chesapeake Bay?
Predicting total nitrogen which leads to dead zones and unlivable habitats for the local fauna using land features, air quality as well as nitrogen oxide. Using a robust and segmented model.
This is an unfinished project for problem 3 that would have needed much more work to have a decent model. There may be some useful code in the EDA/integration of data from NOAA sections.
Visualizing a human-centered watershed network
Chesapeake Bay Data Quadrant (CBDQ) is a data science project that supports Chesapeake Bay conservation by providing four interactive environmental & water quality reports.
A one-stop portal for the Chesapeake Bay community
Greater outreach/education impact for the full range of personal interests and prior knowledge
The data set was cleaned using jupyterlab and then fed into tableau for analysis
Analyzing how changes in land use or land cover have affected the Chesapeake Bay water quality through geospatial data synthesis and machine learning modelling.
Analyzing how environmental factors including land use, land cover, weather, atmosphere, and geology influence water quality, through geospatial data filtration and machine learning modelling.
To make data actionable, it must be accessible, digestible, and robust enough to draw correlations and relationships.
This preliminary analysis shows that pollution loads in the Chesapeake Bay watershed differs between rock types in the region, indicating that geology can be a viable water quality predictor.
Elevated phosphorus level in water is a precursor to harmful algae blooms. Our model predicts total phosphorus in the Chesapeake Watershed Area from water quality metrics.
Exploring Benthic Macroinvertebrates Time Series in Chesapeake Bay Watershed
We created an interactive viz for the Chesapeake Bay, analysed the factors affecting nitrogen & phosphorous pollution with ML, and deployed time-series models to predict future pollution.
The state and federal governments have input large efforts in Chesapeake Bay restoration since 2019. I am starting this project with the hope to see has those efforts paid off?
Powered by open data, stream flow models, and machine learning, this user interface helps communities can make informed decisions for future land development in the Chesapeake Bay Watershed.
Water quality report card for D.C Potomac River community. This's a website-like report card that has used vivid data visulizations to describe the poor water quality and hopefully raise attention.
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