The code is for our paper "Rainfall Spatial Interpolation with Graph Neural Networks", and this paper has been accepted by DASFAA 2023.
For more dataset and baseline details, please refer to our latest work and its code repository: SSIN.
Two real-world hourly raingauge datasets, HK and BW, are collected and used in this paper. Download the processed datasets from Google Drive and place them in the data folder.
In the baselines folder of SSIN, you can find the implementation of IDW, OK, TIN, and TPS:
- IDW: self-implementation.
- OK: by using pykrige.ok.OrdinaryKriging.
- TIN: by using matplotlib.tri.
- TPS: by using scipy.interpolate.Rbf.
For GNN-based baselines, please refer to their original code: KCN and IGNNK.
networks:
- Include files about the network layers and the model architecture.
postprocess:
- Calculate the RMSE, MAE, and NSE for predicted results.
preprocess:
generate_adjs.py: generate adjacency matrix for HK/BW dataset.preprocessing.py: preprocess HK/BW dataset and general thepkldata for training/testing.
utils:
cfg.py: build the argument function.pytorchtools.py: build the EarlyStopping function.utils.py: some useful functions.
training_funcs.py:
- define training-related functions which will be called by the main function of "train_gcn.py" / "train_rc_by_gcn.py" / "train_rc_by_kriging.py".
train_gcn.py:
- train the GSI model to perform spatial interpolation.
train_rc_by_gcn.py:
- reload the trained GSI model for spatial interpolation and perform residual correction by GSI.
train_rc_by_kriging.py:
- reload the trained GSI model for spatial interpolation and perform residual correction by Kriging.
@inproceedings{li2023rainfall,
title={Rainfall Spatial Interpolation with Graph Neural Networks},
author={Li, Jia and Shen, Yanyan and Chen, Lei and Ng, Charles Wang Wai},
booktitle={International Conference on Database Systems for Advanced Applications},
pages={175--191},
year={2023},
organization={Springer}
}
TBD