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Published December 23, 2024 | Version v2
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Fully differentiable, fully distributed River Discharge Prediction: code

Creators

  • 1. ROR icon University of Tübingen

Description

This repository contains the data sets used in: Scholz et al. (2024). Fully differentiable, fully distributed River Discharge Prediction.

  • Create conda environment: conda env create -f environment.yml
  • Configure experiment in config.yaml
  • Run experiment: python train.py
  • Evaluate experiment: Set train: False and name_checkpoint in config.yaml and run python evaluate.py
  • Create attributions: python interpret.py

Files

src.zip

Files (42.2 kB)

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Additional details

Software

Programming language
Python

References

  • Yadan, O.: Hydra - A framework for elegantly configuring complex applications, Github, https://github.com/facebookresearch/hydra, 2019.
  • Hoyer, S. and Hamman, J.: xarray: N-D labeled arrays and datasets in Python, Journal of Open Research Software, 5, https://doi.org/10.5334/jors.148, 2017.
  • Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S.: PyTorch: An Imperative Style, High-Performance Deep Learning Library, p. 12, 2019.