A team of students led by researchers within the Saltwater Intrusion and Sea Level Rise (SWISLR) Research Coordination Network created a geospatial database summarizing the current extent of SWISLR and the current knowledge on SWISLR within the North American Coastal Plain. Students were responsible for mapping scholarly articles, news stories,...
Students collaborated with CEE Professors David Carlson and Mike Bergin to model the effects of land use on the urban heat island effect using satellite imagery and ground-level temperature measurements. Students used machine learning to segment satellite images of Durham, North Carolina by land use. They then paired land use...
A team of students led by Physics professor Dan Scolnic collaborated with Duke Dining leadership to provide an in-depth, quantitative accounting of the carbon footprint of the Duke Dining program. Students used the latest research quantifying CO2 equivalent greenhouse gas emissions for various food types, meals, and sources to produce...
A team of students led by researchers in the Hydroclimatological Lab created a workflow/pipeline for comprehensively estimating the carbon emissions from the Southeastern (SE) United States (US) wetlands using machine learning techniques applied to multi-source data, including field measurements, remote sensing products, and biophysical model outputs. The team first applied...
A team of students led by Civil & Environmental Engineering Professor Helen Hsu-Kim developed a resource reserves database of coal ash wastes stored in hundreds of legacy disposal sites in the United States. The team extracted key information from historical datasets on coal energy production, incorporated geochemical information of coal...
Researchers with the Duke River Center and the Watershed Biogeochemistry Lab investigate patterns of anoxia, or periods of little to no oxygen, in rivers. Oxygen is a necessary element for many organisms to live in rivers, but researchers know little about the timing, duration, and magnitude of low oxygen time...
This project helped to build a globally scalable foundation model to enable near real-time tracking of climate change causes and impacts. A foundation model is a model (usually a deep neural network) that has been trained on a large and diverse set of data, after which it can be adapted...
A team of students led by Prof. Zuchuan Li and co-led by Prof. Nicolas Cassar developed means to estimate the amount of CO2 transferred from the ocean surface to the deep ocean through machine learning techniques applied to satellite data and automatic observations. The team identified variables that can be...
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