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