Introduction
This repo has implemented a pytorch-based encoder-forecaster model with RNNs including (TrajGRU, ConvLSTM) to do precipitation nowcasting. For more information about TrajGRU, please refer to HKO-7.
If you are interested in my implementation of ConvLSTM and TrajGRU, please see ConvLSTM and TrajGRU. It is assumed that the input shape should be . All of my implementation have been proved to be effective in HKO-7 Dataset. Hopefully it helps your research.
Train
Firstly you should apply for HKO-7 Dataset from HKO-7, and modify somelines in config.py to find the dataset path.
Secondly and last, run python3 experiments/trajGRU_balanced_mse_mae/main.py, and then run python3 experiments/trajGRU_frame_weighted_mse/main.py since I have finetuned the model on the basis of model trained in last step.
Environment
Python 3.6+, PyTorch 1.0 and Ubuntu or macOS.
Demo
Performance
The performance on HKO-7 dataset is below.
| CSI | HSS | Balanced MSE | Balanced MAE | ||||||||
| 0.5496 | 0.4772 | 0.3774 | 0.2863 | 0.1794 | 0.6713 | 0.6150 | 0.5226 | 0.4253 | 0.2919 | 5860.97 | 15062.46 |
Download
Citation
@inproceedings{xingjian2017deep,
title={Deep learning for precipitation nowcasting: a benchmark and a new model},
author={Shi, Xingjian and Gao, Zhihan and Lausen, Leonard and Wang, Hao and Yeung, Dit-Yan and Wong, Wai-kin and Woo, Wang-chun},
booktitle={Advances in Neural Information Processing Systems},
year={2017}
}
@inproceedings{xingjian2015convolutional,
title={Convolutional LSTM network: A machine learning approach for precipitation nowcasting},
author={Xingjian, SHI and Chen, Zhourong and Wang, Hao and Yeung, Dit-Yan and Wong, Wai-Kin and Woo, Wang-chun},
booktitle={Advances in neural information processing systems},
pages={802--810},
year={2015}
}