| --- |
| language: en |
| license: mit |
| tags: |
| - precipitation |
| - convlstm |
| - multitask-learning |
| - climate |
| - vegetation |
| - amazon |
| model-index: |
| - name: MultiTask ConvLSTM w/veg inputs |
| results: |
| - task: |
| type: time-series-forecasting |
| name: Precipitation Prediction |
| dataset: |
| name: ERA5-Land Amazon Basin (2021–2023) |
| type: reanalysis |
| metrics: |
| - type: mean_squared_error |
| value: 0.28 |
| - type: spearman_correlation |
| value: 0.87 |
| - type: pearson_correlation |
| value: 0.79 |
| - type: kendall_tau |
| value: 0.70 |
| - type: nash_sutcliffe_efficiency |
| value: 0.62 |
| - type: f1 |
| value: 0.82 |
| - type: accuracy |
| value: 0.90 |
| - type: precision |
| value: 0.90 |
| - type: ROC-AUC |
| value: 0.97 |
| - type: recall |
| value: 0.75 |
| --- |
| |
| # MultiTask ConvLSTM for Precipitation Prediction |
|
|
| This repository contains two MultiTask ConvLSTM models: |
| - **veg/**: Model trained with vegetation input variables |
| - **noveg/**: Model trained without vegetation input variables |
|
|
| Both directories include: |
| - `convlstm.py`: base ConvLSTM layers |
| - `model.py`: MultiTask ConvLSTM model definition |
| - `example_inference.py`: inference script |
| - `data/`: example `.pth` files (test) |
|
|
| These scripts are provided for reproducibility of the model architecture and workflow. |
| Exact runtime and performance may vary depending on hardware. |
|
|
| ## Example Data |
|
|
| We provide a large test `.pth` files |
| so you can immediately run the inference script without preprocessing. |
| These files are already preprocessed and normalized from the ECWMF REA5 reanalysis data. |
|
|
| Each `.pth` file loads as a list of batches: |
|
|
| - `X_batch`: shape `(B, T_in, C_in, H*W)` |
| - `y_batch`: shape `(B, T_out, C_out, H*W)` |
| - `y_zero_batch`: shape `(B, T_out, C_out, H*W)` |
|
|
| with `H=81`, `W=97`. Inside `evaluate(...)`, these are reshaped to `(B, T, C, H, W)`. |
|
|
| --- |
|
|
| ## How to Use |
|
|
| Ensure all files are in the correct directory then run the example_inference.py file. |
| |
| # 1 Get the repo |
| git clone https://huggingface.co/<your-username>/MultiTaskConvLSTM |
| cd MultiTaskConvLSTM |
| |
| # 2 Install minimal deps |
| pip install -r requirements.txt |
| |
| # 3 Run inference (choose one variant) |
| python veg/example_inference.py |
| # or |
| python noveg/example_inference.py |
| |
| |
| ## Citation If you use this model, please cite: > Lilly Horvath-Makkos (2025). [title] [journal] BibTeX: |
| bibtex |
| @article{horvathmakkos2025, |
| title={Title}, |
| author={Horvath-Makkos, Lilly}, |
| journal={Journal}, |
| year={2025} |
| } |
