Modular Compositional Learning Improves 1D Hydrodynamic Lake Model Performance by Merging Process-Based Modeling With Deep Learning

R. Ladwig, A. Daw, E. A. Albright, C. Buelo, A. Karpatne, M. F. Meyer, A. Neog, P. C. Hanson, H. A. Dugan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Hybrid Knowledge-Guided Machine Learning (KGML) models, which are deep learning models that utilize scientific theory and process-based model simulations, have shown improved performance over their process-based counterparts for the simulation of water temperature and hydrodynamics. We highlight the modular compositional learning (MCL) methodology as a novel design choice for the development of hybrid KGML models in which the model is decomposed into modular sub-components that can be process-based models and/or deep learning models. We develop a hybrid MCL model that integrates a deep learning model into a modularized, process-based model. To achieve this, we first train individual deep learning models with the output of the process-based models. In a second step, we fine-tune one deep learning model with observed field data. In this study, we replaced process-based calculations of vertical diffusive transport with deep learning. Finally, this fine-tuned deep learning model is integrated into the process-based model, creating the hybrid MCL model with improved overall projections for water temperature dynamics compared to the original process-based model. We further compare the performance of the hybrid MCL model with the process-based model and two alternative deep learning models and highlight how the hybrid MCL model has the best performance for projecting water temperature, Schmidt stability, buoyancy frequency, and depths of different isotherms. Modular compositional learning can be applied to existing modularized, process-based model structures to make the projections more robust and improve model performance by letting deep learning estimate uncertain process calculations.

Original languageEnglish
Article numbere2023MS003953
JournalJournal of Advances in Modeling Earth Systems
Volume16
Issue number1
DOIs
StatePublished - Jan 2024
Externally publishedYes

Funding

Lake data were obtained from the North Temperate Lakes Long‐Term Ecological Research program (#DEB‐1440297 and #DEB‐2025982). The project was supported through a United States National Science Foundation (NSF) ABI development Grant (DBI 1759865), UW‐Madison Data Science Initiative grant, and NSF MSB Grant (2213549). RL was funded by UW‐Madison's Integrative Biology Postdoctoral Fellowship. MFM was funded by a Mendenhall Fellowship from the U.S. Geological Survey Water Mission Area. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. EAA was supported by the NSF Graduate Research Fellowship Program (DGE‐1747503) with additional funding from the Wisconsin Alumni Research Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We are thankful for the feedback from USGS‐initiated peer reviews by Shuqi Lin and Sebastiano Piccolroaz, which vastly improved this manuscript. Further, we would like to thank two anonymous reviewers for taking the necessary time and effort to review the manuscript. We sincerely appreciate all your valuable comments and suggestions, which helped us in improving the quality of the manuscript. Lake data were obtained from the North Temperate Lakes Long-Term Ecological Research program (#DEB-1440297 and #DEB-2025982). The project was supported through a United States National Science Foundation (NSF) ABI development Grant (DBI 1759865), UW-Madison Data Science Initiative grant, and NSF MSB Grant (2213549). RL was funded by UW-Madison's Integrative Biology Postdoctoral Fellowship. MFM was funded by a Mendenhall Fellowship from the U.S. Geological Survey Water Mission Area. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. EAA was supported by the NSF Graduate Research Fellowship Program (DGE-1747503) with additional funding from the Wisconsin Alumni Research Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We are thankful for the feedback from USGS-initiated peer reviews by Shuqi Lin and Sebastiano Piccolroaz, which vastly improved this manuscript. Further, we would like to thank two anonymous reviewers for taking the necessary time and effort to review the manuscript. We sincerely appreciate all your valuable comments and suggestions, which helped us in improving the quality of the manuscript.

Keywords

  • deep learning
  • hydrodynamics
  • knowledge-guided machine learning
  • lake model
  • modular compositional learning
  • water temperature

Fingerprint

Dive into the research topics of 'Modular Compositional Learning Improves 1D Hydrodynamic Lake Model Performance by Merging Process-Based Modeling With Deep Learning'. Together they form a unique fingerprint.

Cite this