Articles | Volume 14, issue 12
Data description paper
20 Dec 2022
Data description paper |  | 20 Dec 2022

WaterBench-Iowa: a large-scale benchmark dataset for data-driven streamflow forecasting

Ibrahim Demir, Zhongrun Xiang, Bekir Demiray, and Muhammed Sit

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Cited articles

Agliamzanov, R., Sit, M., and Demir, I.: Hydrology@ Home: a distributed volunteer computing framework for hydrological research and applications, J. Hydroinform., 22, 235–248, 2020. 
Athira, V., Geetha, P., Vinayakumar, R., and Soman, K. P.: Deepairnet: Applying recurrent networks for air quality prediction, Proc. Comput. Sci., 132, 1394–1403, 2018. 
Bai, Y., Bezak, N., Sapač, K., Klun, M., and Zhang, J.: Short-term streamflow forecasting using the feature-enhanced regression model, Water Resour. Manage., 33, 4783–4797, 2019. 
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling, arXiv [preprint],, 2014. 
Cybenko, G.: Approximation by superpositions of a sigmoidal function, Math. Control Signal., 2, 303–314, 1989. 
Short summary
We provide a large benchmark dataset, WaterBench-Iowa, with valuable features for hydrological modeling. This dataset is designed to support cutting-edge deep learning studies for a more accurate streamflow forecast model. We also propose a modeling task for comparative model studies and provide sample models with codes and results as the benchmark for reference. This makes up for the lack of benchmarks in earth science research.
Final-revised paper