The dataset include hydroclimate and ambient environmental as the input data and Cyanobacterial HABs Index (CI) calculated from satellite imageries as the output data altogether used to train and validate three data-driven (machine learning) models and their Ensemble Average (AE) to predict HABs cell count in southwest Lake Erie. The data also include HABs volumetric and areal concentrations obtained from literature and used in conjunction with the CI calculated from satellite data to develop statistical regression models for use to convert model predicted CI values (cell counts) to volumetric/areal concentrations of HABs.

This data release contains one dataset and one model archive in support of the journal article "Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies" by Jennifer C. Murphy and Jeffrey G. Chanat. The model archive contains scripts (run in R) to reproduce the four machine learning models (logistic regression, linear and quadratic discriminant analysis, and k-nearest neighbors) trained and tested as part of the journal article. The dataset contains the estimated probabilities for each of these models when applied to a training and test dataset.

The water and climate data for Lake Erie, including: Soil moisture, streamflow, water temperature, evaporation, baseflow. NOTE: This dataset has been removed from public access due to revocation. Please refer inquiries regarding this dataset to the listed contact person.

The study results and data used and produced in this study are available through the Texas Data Repository at https://doi.org/10.18738/T8/A9X5ET (Srinivasan et al., 2023). The data also includes the necessary information to reproduce the figures and tables presented in the study. This dataset is associated with the following publication: Bawa, A., K. Mendoza, R. Srinivasan, F. O'Donncha, D. Smith, K. Wolfe, R. Parmar, J. Johnston, and J. Corona. Enhancing Hydrological Modeling of Ungauged Watersheds through Machine Learning and Physical Similarity-based Regionalization of Calibration Parameters. ENVIRONMENTAL MODELLING & SOFTWARE. Elsevier Science, New York, NY, 186: 106335, (2025).

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 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 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).

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 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 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).

This dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from North Temperate Lakes Long-TERM Ecological Research Program (NTL-LTER; https://lter.limnology.wisc.edu/). The buoy is supported by both the Global Lake Ecological Observatory Network (gleon.org) and the NTL-LTER. 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).

This dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from North Temperate Lakes Long-TERM Ecological Research Program (NTL-LTER; https://lter.limnology.wisc.edu/). The buoy is supported by both the Global Lake Ecological Observatory Network (gleon.org) and the NTL-LTER. 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).

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 for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).