Skip to main content
Advanced Search

Filters: Tags: deep learning (X)

78 results (10ms)   

Filters
Date Range
Extensions
Types
Contacts
Categories
Tag Types
Tag Schemes
View Results as: JSON ATOM CSV
thumbnail
These data were collected to support the development of detection and classification algorithms to support Bureau of Ocean Energy Management (BOEM) studies and assessments associated with offshore wind energy production. There are 3 child zip files included in this data release. 01_Codebase.zip contains a codebase for using deep learning to filter images based on the probability of any bird occurrence. It includes instructions and files necessary for training, validating, and testing a machine learning detection algorithm. 02_Imagery.zip contains imagery that were collected using a Partenavia P68 fixed-wing airplane using a PhaseOne iXU-R 180 forward motion compensating 80-megapixel digital frame camera with...
thumbnail
This section provides code for reproducing the figures in Rahmani et al. (2023b). The full model archive is organized into these four child items: 1. Model code - Python files and README for reproducing model training and evaluation 2. Inputs - Basin attributes and shapefiles, forcing data, and stream temperature observations 3. Simulations - Simulation descriptions, configurations, and outputs [THIS ITEM] 4. Figure code - Jupyter notebook to recreate the figures in Rahmani et al. (2023b) The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling....
thumbnail
Observed water temperatures from 1980-2019 were compiled for 2,332 lakes in the US. These data were used as training, test, and error-estimation data for process-guided deep learning models and the evaluation of process-based models. The data are formatted as a single csv (comma separated values) file with attributes corresponding to the unique combination of lake identifier, time, and depth. Data came from a variety of sources, including the Water Quality Portal, the North Temperate Lakes Long-Term Ecological Research Project, and digitized temperature records from the MN Department of Natural Resources. This dataset is part of a larger data release of lake temperature model inputs and outputs for these same lakes...
thumbnail
Daily lake surface temperatures estimates for 185,549 lakes across the contiguous United States from 1980 to 2020 generated using an entity-aware long short-term memory deep learning model. In-situ measurements used for model training and evaluation are from 12,227 lakes and are included as well as daily meteorological conditions and lake properties. Median per-lake estimated error found through cross validation on lakes with in-situ surface temperature observations was 1.24 °C. The generated dataset will be beneficial for a wide range of applications including estimations of thermal habitats and the impacts of climate change on inland lakes.
Categories: Data; Tags: AL, AR, AZ, Alabama, Aquatic Biology, All tags...
thumbnail
Using predicted lake temperatures from uncalibrated, process-based models (PB0) and process-guided deep learning models (PGDL), this dataset summarized a collection of thermal metrics to characterize lake temperature impacts on fish habitat for 881 lakes. Included in the metrics are daily thermal optical habitat areas and a set of over 172 annual thermal metrics.
thumbnail
This dataset provides model specifications used to estimate water temperature from a process-based model (Hipsey et al. 2019). The format is a single JSON file indexed for each lake based on the "site_id". This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
thumbnail
This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of...
thumbnail
Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added...
thumbnail
This dataset includes model inputs that describe weather conditions for the 68 lakes included in this study. Weather data comes from gridded estimates (Mitchell et al. 2004). There are two comma-separated files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs...
thumbnail
Observed water temperatures from 1980-2018 were compiled for 877 lakes in Minnesota (USA). There were four lakes included in this data release that did not have temperature observations available at the time of compilation or these data existed elsewhere and were unknown to the compilation team. These data were used as training, test, and error-estimation data for process-guided deep learning models and the evaluation of process-based models. The data are formatted as a single csv (comma separated values) file with attributes corresponding to the unique combination of lake identifier, time, and depth. Data came from a variety of sources, including the Water Quality Portal, the North Temperate Lakes Long-Term Ecological...
thumbnail
Daily maximum water temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish species. This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper DRB. The modeling approach includes process-guided deep learning and data assimilation (Zwart et al., 2023). The model is driven by weather forecasts and observed reservoir releases and produces maximum water temperature forecasts for the issue day (day 0) and 7 days into the future (days 1-7). In combination with data provided in Oliver et al. (2022),...
thumbnail
This dataset includes model inputs (specifically, weather and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
thumbnail
Water temperature estimates from multiple models were evaluated by comparing predictions to observed water temperatures. The performance metric of root-mean square error (in degrees C) is calculated for each lake and each model type, and matched values for predicted and observed temperatures are also included to support more specific error estimation methods (for example, calculating error in a particular month). Errors for the process-based model are compared to predictions as shared in Model Predictions data since these models were not calibrated. Errors for the process-guided deep learning models were calculated from validation folds and therefore differ from the comparisons to Model Predictions because those...
thumbnail
This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper Delaware River Basin. This section contains forcing data for water temperature forecasting models reported in Zwart et al. (2023), including a process-based pre-trainer, gridded weather and forecasted weather data, and flow and temperature for reservoir inlets and outlets.
thumbnail
This dataset provides shapefile outlines of the 2,332 lakes that had temperature modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). A csv file of lake metadata is included, which includes lake metadata and all features that were considered for the meta transfer model (not all meta features were used). This dataset is part of a larger data release of lake temperature model inputs and outputs for 2,332 lakes in the U.S. (https://doi.org/10.5066/P9I00WFR).
This data release component contains water temperature predictions in 118 river catchments across the U.S. Predictions are from the four models described by Rahmani et al. (2020): locally-fitted linear regression, LSTM-noQ, LSTM-obsQ, and LSTM-simQ.
thumbnail
This dataset provides shapefile outlines of the 881 lakes that had temperature modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). A csv file of lake metadata is also included. This dataset is part of a larger data release of lake temperature model inputs and outputs for 881 lakes in the U.S. state of Minnesota (https://doi.org/10.5066/P9PPHJE2).
thumbnail
This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to train and validate all temperature models. The model training period was from 2010-10-01 to 2014-09-30, and the test period was from 2014-10-01 to 2016-09-30.
Categories: Data; Tags: AL, AR, AZ, Alabama, Arizona, All tags...


map background search result map search result map Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 1 Lake information for 881 lakes Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 2 Water temperature observations Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 6 model evaluation Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 7 thermal and optical habitat estimates Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 1 Lake information for 2,332 lakes Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 2 Water temperature observations Predicting water temperature in the Delaware River Basin: 1 Waterbody information for 456 river reaches and 2 reservoirs Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 3 Model inputs Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 5 Model predictions Daily surface temperature predictions for 185,549 U.S. lakes with associated observations and meteorological conditions (1980-2020) 2 Observations: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 2) model driver data 4. Figure code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling Code, imagery, and annotations for training a deep learning model to detect wildlife in aerial imagery Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 2) model driver data Predicting water temperature in the Delaware River Basin: 1 Waterbody information for 456 river reaches and 2 reservoirs Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 7 thermal and optical habitat estimates Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 1 Lake information for 881 lakes Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 2 Water temperature observations Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 6 model evaluation Code, imagery, and annotations for training a deep learning model to detect wildlife in aerial imagery Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 2 Water temperature observations Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 1 Lake information for 2,332 lakes Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 3 Model inputs Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 5 Model predictions 2 Observations: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins 4. Figure code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling Daily surface temperature predictions for 185,549 U.S. lakes with associated observations and meteorological conditions (1980-2020)