oklbuy331 / Spatiotemporal-Prediction

Spatiotemporal-Prediction

黏土心墙坝渗流监测数据的深度学习方法研究

This repository contains the code for the reproducibility of the experiments presented in the dissertation "Research on Deep Learning Model for Seepage Safety Monitoring of Clay-core Wall Dams". Authors: Pan Liao, Xiaoqing Li

Installation

We provide a requirements list with all the project dependencies in requirements.txt.

Configuration files

The config/ directory stores all the configuration files used to run the experiment. config/ stores model configurations used for experiments on imputation.

Experiments

The scripts used for the experiment in the paper are in the run_experiment.py and run_inference.py.

• run_experiment.py is used to compute the metrics for the deep pore water pressure prediction methods. An example of usage for GAT-TCN model is

   python run_experiment.py --config gat_tcn.yaml --model-name gat_tcn --dataset-name grin

• run_inference.py is used for the experiments on sparse datasets using pre-trained models. An example of usage is

   python run_inference.py --config inference.yaml --model-name gat_tcn --dataset-name grin --exp-name 20230509T123018_21380673