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Found 21 dataset(s) matching "cubist".
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Anthropogenic hydrologic alteration threatens the health of riverine ecosystems. This study assesses hydrologic alteration in the Pearl and Pascagoula river basins using modeled daily streamflow....
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A water use study was conducted to understand the drivers of historical water use in the Providence Water Supply Board network (the service area of Providence Water Supply Board and its wholesale...
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7Q10 records and basin characteristics for 224 basins in South Carolina, Georgia, and Alabama (2015)
This data release provides the data and R scripts used for the 2018 publication titled "Improving predictions of hydrological low-flow indices in ungaged basins using machine learning",...
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Results from generalized additive models (GAM), random forest models (RFM), and cubist models (CUB) for three Dauphin Island Sealab (DIS) operated salinity sites in Mobile Bay are reported in this...
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A multiple machine-learning model (Asquith and Killian, 2024) implementing Cubist and Random Forest regressions was used to predict monthly mean groundwater levels through time for the available...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The...
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This data release is a subset of the 2010 LANDFIRE Existing Vegetation Cover, covering the Russian River watershed. This LANDFIRE data was downloaded and processed in 2014. The LANDFIRE existing...
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The Existing Vegetation Cover (EVC) product depicts percent canopy cover by life form and is an important input to other LANDFIRE mapping efforts. EVC is generated separately for tree, shrub and...
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We integrated 250-m enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) with land cover, biogeophysical (e.g., soils, topography) and...
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Defining site potential for an area establishes its possible long-term vegetation growth productivity in a relatively undisturbed state, providing a realistic reference point for ecosystem...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The...
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<div style='text-align:Left;'><div><div><p><span style='font-size:12pt'>These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing...
