Fully differentiable, fully distributed River Discharge Prediction: code
Scholz, Fedor; Traub, Manuel; Zarfl, Christiane; Scholten, Thomas; Butz, Martin V.;
Fully differentiable, fully distributed River Discharge Prediction: code
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
- University of Tübingen Germany
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
selected citations
Citations provided by BIP!
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP! 0
Average
Average
Average