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---
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}
} |