/Water-Table-Depth-Prediction-PyTorch

Implement paper using PyTorch

Primary LanguagePythonMIT LicenseMIT

Water-Table-Depth-Prediction-Pytorch

Introduction

This is a PyTorch implementation of our work Developing a Long Short-Term Memory (LSTM) based Model for Predicting Water Table Depth in Agricultural Areas.[Paper]

Requirements

Python3.x
pytorch>=0.4.0
numpy>=1.14.0
pandas>=0.22.0
scikit-learn>=0.14

Installation

The code was tested with Python 3.5. To use this code, please do:

  1. Clone the repo:

    git https://github.com/jfzhang95/Water-Table-Depth-Prediction-PyTorch
    cd Water-Table-Depth-Prediction-PyTorch
  2. Install dependencies:

    pip install matplotlib numpy pandas scikit-learn

   For pytorch installation, please see in PyTorch.org.

  1. To try the demo code, please run:
    python demo.py

If installed correctly, the result should look like this: results

Noted that the demo data (demo.csv) are processed manually, so they are not real data, but they still can reflect the correlation between the original data.

Citation

If you think our code is useful, please consider citing the following paper:

@article{zjf18,
  journal        = {Journal of Hydrology},
  title          = {Developing a Long Short-Term Memory (LSTM) based Model for Predicting Water Table Depth in Agricultural Areas},
  author         = {Jianfeng Zhang, Yan Zhu, Xiaoping Zhang, Ming Ye and Jinzhong Yang},
  year           = {2018},
  volume         = {561},
  pages          = {918-929}
}

License

MIT