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7 results for “green stormwater infrastructure”
PREDICTING THE PERFORMANCE OF GREEN STORMWATER INFRASTRUCTURE USING MULTIVARIATE LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORK
<p>The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With numerical models, the choice of a spatio-temporal discretization scheme for the computational domain is a strenuous task that requires extensive calibration and potentially lab-based parameters and experimentation. The performance of GSI has high temporal dynamics due to natural, anthropogenic, and climatic processes that are not well represented by the traditional physics-based hydrologic models, which are calibrated against only a few historical observations and have a user-defined and constrained set of computational outcomes. Deep learning-based predictive models, such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity to quantify GSI performance, accounting for its highly dynamic and constantly evolving nature by leveraging advancements in observational data. A LSTM regression can overcome some of the limitations associated with traditional hydrological models to aid the development of a fully data-informed GSI performance predictor. To demonstrate the LSTM and traditional model outcomes, both methods were applied to a rain garden in Villanova, PA, USA. Specifically, a LSTM model was used to predict the recession of ponded water depth in the rain garden using five years of observed data.</p>
Green stormwater infrastructure projects voluntarily installed in Baltimore City through 2019
Much of the green stormwater infrastructure (GSI) in Baltimore, Maryland, USA, has been installed voluntarily by nonprofits and community groups, yet no comprehensive record of these installations previously existed. We worked with nonprofit stakeholders and Baltimore’s Department of Public Works to compile such a record, using both information provided by these agencies and publicly available data sources such as annual reports and newspaper articles. This dataset includes all voluntary green stormwater infrastructure projects that we were able to identify by the end of 2019, with the first known installation completed in 2001. The dataset includes two data tables, one with project-level information, and one with the locations of individual GSI facilities included in each project.
Concentration of metals and base cations in green stormwater infrastructure soils
<p>Green stormwater infrastructure (GSI) is adopted to reduce the impact of stormwater on urban flooding and water quality issues. Traditional methods use inflow versus outflow metal and base cation concentrations from water samples at inlet and outlet to determine accumulation in GSI basins which can be expensive sometimes. Soil sampling could be a more cost-effective and time-averaged approach in evaluating the accumulation of metals and base cations in GSI compared to the traditional methods. This dataset presents data from twenty-one GSI basins soils located in New York and Pennsylvania, USA. The dataset contains a description of GSI basins considered in the study and the concentration of metals and base cations in those basins. The dataset includes concentrations of 3 base cations (Ca, Mg, Na) and 6 metals (Cd, Cr, Cu, Ni, Pb, and Zn). Various GSI basin characteristics are included in the dataset which includes information such as age of the basins, sources of runoff draining into the GSI basins, and drainage area ratio (ratio of the area draining into a GSI basin to the area of the GSI basin itself).</p>
Concentration of metals and base cations in green stormwater infrastructure soils
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Data from: Not always an amenity: Green stormwater infrastructure provides highly variable ecosystem services in both regulatory and voluntary contexts
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Optimal Green Infrastructure: Reducing Stormwater Pollution in Maunalua Bay, O'ahu, Hawai'i
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UArizona Campus Living Lab Green Stormwater Infrastructure Data
<p>Meteorological dataset from UA Campus Living Lab Green Stormwater Infrastructure (GSI) initiatve. To request access to the most recent data, fill out this form: <a title="GSI Living Lab Data Request Form" href="https://forms.gle/YzP5WLWPMGz7dqox8">GSI Living Lab Data Request Form</a>. </p> <p>Data are generated at three sites on campus: Old Main, Gould Simpson, and Physics and Atmospheric Sciences. These sites represent various green stormwater infrastructure designs to capture and water plants. Old Main represents a native xeroscape rain garden with the primary water source being rooftop runoff. Gould Simpson is a Resilience Garden sponsored by the University of Arizona Arboretum featuring legume trees that receives water from two rooftops and surrounding hardscapes. Physics and Atmospheric Sciences features a classic southwest landscape with slight grading for stormwater collection and desert vegetation. For more information, visit the University of Arizona's GSI <a href="http://gicampuslivinglab.arizona.edu/">Campus Living Lab</a> website.</p> <p>In collaboration with the University of Arizona's Department of Communications & Cyber Tech Data Science Institute, we created an interactive dashboard to display current micrometeorological data, to view the data dashboard visit the <a href="https://viz.datascience.arizona.edu/gsi-dashboard/">GSI Campus Living Lab Dashboard</a>.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.