Attention-based Models for Snow-Water Equivalent Prediction

Krishu K Thapa¹, Bhupinderjeet Singh¹, Supriya Savalkar¹, Alan Fern², Kirti Rajagopalan¹, Ananth Kalyanaraman¹

¹ Washington State University, Pullman, WA. ² Oregon State University, Corvallis, OR.

Predicting the SWE value for multiple SNOTEL locations in the Western US using the Attention Models

Model Folder

• Spatial_Attention.py - This file has the code for the spatial attention implementation along with training and testing. The data is loaded from the SDL.py file inside the DataLoader folder.

• Temporal_Attention.py - This file has the code for the temporal attention implementation along with training and testing. The data is loaded from the TDL.py file inside the DataLoader folder.

DataLoader

• Data: This has all the data we have used in our model implementation for the SNOTEL locations.
• SDL.py: This is the data loader file for the spatial model. It returns the training and testing data for the Spatial Attention model.
• TDL.py: This is the data loader file for the temporal model. It returns the training and testing data for the Temporal Attention model.
• feature_prep.py: This file processes all the raw data and generates the processed csv files of data which are used by the data loaders for spatial and attention model.

Citation

If you use our idea in your research, please cite:

Thapa, Krishu & Singh, Bhupinderjeet & Savalkar, Supriya & Fern, Alan & Rajagopalan, Kirti & Kalyanaraman, Ananth. (2024). Attention-Based Models for Snow-Water Equivalent Prediction. Proceedings of the AAAI Conference on Artificial Intelligence. 38. 22969-22975. 10.1609/aaai.v38i21.30337. https://doi.org/10.1609/aaai.v38i21.30337