Process-guided deep learning for lake water temperature prediction
Initial public release of the code for base modeling, pretraining, ensemble learning, and process-guided loss design for lake water temperature prediction in Lake Mendota. This repository includes: Base model training and evaluation Pretraining on GLM simulation data and finetuning on observations Depth-wise ensemble modeling Process-guided loss with energy conservation constraint GLM simulation scripts Monthly energy balance validation scripts Notes: The repository was developed primarily for Google Colab + Google Drive. The current implementation is intended mainly for comparative evaluation across model settings. Some paths may need to be
Resource information
- Format
- zip
- Original repository
- 19758546
- DOI
- 10.5281/zenodo.19758546
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- mit-license
- Last record update
- Nov 5, 2026
About this resource
Initial public release of the code for base modeling, pretraining, ensemble learning, and process-guided loss design for lake water temperature prediction in Lake Mendota. This repository includes: Base model training and evaluation Pretraining on GLM simulation data and finetuning on observations Depth-wise ensemble modeling Process-guided loss with energy conservation constraint GLM simulation scripts Monthly energy balance validation scripts Notes: The repository was developed primarily for Google Colab + Google Drive. The current implementation is intended mainly for comparative evaluation across model settings. Some paths may need to be adapted for a different local or cloud environment. Citation: Please cite this repository using the information provided in CITATION.cff.