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  7. Process-guided deep learning predictions of lake water temperature
  8. Process-guided deep learning water temperature predictions: 5 Model prediction data
  9. Process-guided deep learning water temperature predictions: 5a Lake Mendota detailed prediction data

Process-guided deep learning water temperature predictions: 5a Lake Mendota detailed prediction data

Dates

Publication Date
Start Date
1980-04-01
End Date
2018-12-31

Citation

Read, J.S., Jia, X., Willard, J., Appling, A.P., Zwart, J.A., Oliver, S.K., Karpatne, A., Hansen, G.J.A., Hanson, P.C., Watkins, W., Steinbach, M., and Kumar, V., 2019, Data release: Process-guided deep learning predictions of lake water temperature: U.S. Geological Survey data release, https://doi.org/10.5066/P9AQPIVD.

Summary

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 physical constraint [...]

Contacts

Point of Contact :
Jordan S. Read, U.S. Geological Survey
Originator :
Jordan S. Read, Xiaowei Jia, Jared Willard, Alison P. Appling, Jacob A. Zwart, Samantha K. Oliver, Anuj Karpatne, Gretchen J.A. Hansen, Paul C. Hanson, William Watkins, Michael Steinbach, Vipin Kumar
Metadata Contact :
Jordan S. Read, U.S. Geological Survey
Publisher :
U.S. Geological Survey
Distributor :
U.S. Geological Survey - ScienceBase

Attached Files

Click on title to download individual files attached to this item.

05a_prediction_me.xml
Original FGDC Metadata
View 		39.38 KB application/fgdc+xml
me_predict_pb0.csv 12.64 MB text/csv
me_season_predict_dl.csv 14.39 MB text/csv
me_season_predict_pb.csv 16.39 MB text/csv
me_season_predict_pgdl.csv 14.39 MB text/csv
me_similar_predict_dl.csv 72.69 MB text/csv
me_similar_predict_pb.csv 98.32 MB text/csv
me_similar_predict_pgdl.csv 86.72 MB text/csv
me_year_predict_dl.csv 14.52 MB text/csv
me_year_predict_pb.csv 16.35 MB text/csv
me_year_predict_pgdl.csv 14.38 MB text/csv

Purpose

Fisheries biology, limnological research, and climate science.

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Communities

  • National and Regional Climate Adaptation Science Centers
  • Northeast CASC

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