NeuralZoo | A library for forecasting meteorological variables

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Lu Li

Read the docs | Try it by yourself!

Installation

MetReg support Python 3.8+. To install, you can use pip or conda.

Latest Release

Install the latest release using pip.

pip3 install NeuralZoo

Development Version

If you prefer the latest dev version, clone this repository and run the following command from the top-most folder of the repository. These commandwill build new environment and install NeuralZoo.

make venv
export PYTHONPATH=$PYTHONPATH:[abspath of home dir]/NeuralZoo

Requirements

NeuralZoo requires common used packages for machine learning. If you face any problems, try installing dependencies manually.

make source
make init

Citing

If you find NeuralZoo useful for your research work, please cite us as follows:

• HybridHydro: Li, Lu et al.(2022) "Soil Moisture Forecasting integrating Physical-based Model with Deep Learning." Journal of Hydrometeorology.
• CLSTM: Li, Lu et al.(2022). "Causality-Structured Deep Learning for Soil Moisture Predictions." Journal of Hydrometeorology.
• AttConvLSTM: Li, Lu et al.(2022). "Multistep forecasting of soil moisture using a spatiotemporal deep encoder-decoder networks." Journal of Hydrometeorology.
• RF-Granger: Li, Lu, et al.(2020). "A causal inference model based on random forests to identify the effect of soil Moisture on precipitation." Journal of Hydrometeorology.
• Comparative study: Pan, J., et al.(2019). Using data‐driven methods to explore the predictability of surface soil moisture with FLUXNET site data. Hydrological Processes.