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Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the...
The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to... -
Data release: Process-guided deep learning predictions of lake water temperature
<p>Climate change has been shown to influence lake temperatures in different ways. To better understand the diversity of lake responses to climate change and give managers... -
Waveform Data and Metadata used to National Earthquake Information Center...
This is the supporting data used to train machine learning models used by the National Earthquake Information Center to improve pick times and classify source characteristics. -
Process-guided deep learning water temperature predictions: 3a Lake Mendota inputs
This dataset includes model inputs that describe local weather conditions for Lake Mendota, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded... -
Process-guided deep learning water temperature predictions: 6c All lakes...
This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were... -
Process-guided deep learning water temperature predictions: 5 Model prediction data
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-... -
Process-guided deep learning water temperature predictions: 4 Training data
This dataset includes compiled water temperature data from a variety of sources, including the Water Quality Portal (Read et al. 2017), the North Temperate Lakes Long-TERM... -
Process-guided deep learning water temperature predictions: 5a Lake Mendota...
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-... -
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 3...
This dataset provides model parameters used to estimate water temperature from a process-based model (Hipsey et al. 2019) using uncalibrated model configurations (PB0) and the... -
1 Site Information: Deep learning approaches for improving prediction of...
<p>This data release component contains shapefiles of river basin polygons and monitoring site locations coincident with the outlets of those basins. A table of basin... -
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 6...
Water temperature estimates from multiple models were evaluated by comparing predictions to observed water temperatures. The performance metric of root-mean square error (in... -
Process-guided deep learning water temperature predictions: 4c All lakes...
Observed water temperatures from 1980-2018 were compiled for 68 lakes in Minnesota and Wisconsin (USA). These data were used as training data for process-guided deep learning... -
Process-guided deep learning water temperature predictions: 4a Lake Mendota...
This dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from North... -
EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic...
This package contains a 3D Seismic velocity model and an updated microseismic catalog associated with a proceedings paper (Chai et al., 2020) published in the 45th Workshop on... -
Data release: Process-guided deep learning predictions of lake water temperature
Climate change has been shown to influence lake temperatures in different ways. To better understand the diversity of lake responses to climate change and give managers tools to... -
Process-guided deep learning water temperature predictions: 5a Lake Mendota...
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-... -
Process-guided deep learning water temperature predictions: 4a Lake Mendota...
This dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from North... -
Process-guided deep learning water temperature predictions: 6 Model...
This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were... -
Process-guided deep learning water temperature predictions: 2 Model...
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... -
Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs
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...