WaterBench-Iowa: a large-scale benchmark dataset for data-driven streamflow forecasting
<p>This study proposes a comprehensive benchmark dataset for streamflow forecasting, WaterBench-Iowa, that follows FAIR (findability, accessibility, interoperability, and reuse) data principles and is prepared with a focus on convenience for utilizing in data-driven and machine learning studie...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-12-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/14/5605/2022/essd-14-5605-2022.pdf |
| Summary: | <p>This study proposes a comprehensive benchmark dataset for
streamflow forecasting, WaterBench-Iowa, that follows FAIR (findability, accessibility, interoperability, and reuse) data principles
and is prepared with a focus on convenience for utilizing in data-driven
and machine learning studies, and provides benchmark performance for
state of art deep learning architectures on the dataset for comparative
analysis. By aggregating the datasets of streamflow, precipitation,
watershed area, slope, soil types, and evapotranspiration from federal
agencies and state organizations (i.e., NASA, NOAA, USGS, and Iowa Flood
Center), we provided the WaterBench-Iowa for hourly streamflow forecast
studies. This dataset has a high temporal and spatial resolution with rich
metadata and relational information, which can be used for a variety of deep learning and machine learning research. We defined a sample benchmark task
of predicting the hourly streamflow for the next 5 d for future
comparative studies, and provided benchmark results on this task with sample
linear regression and deep learning models, including long short-term memory
(LSTM), gated recurrent units (GRU), and sequence-to-sequence (S2S). Our
benchmark model results show a median Nash-Sutcliffe efficiency (NSE) of 0.74 and a median Kling-Gupta efficiency (KGE) of 0.79
among 125 watersheds for the 120 h ahead streamflow prediction task.
WaterBench-Iowa makes up for the lack of unified benchmarks in earth science research and can be accessed at Zenodo <a href="https://doi.org/10.5281/zenodo.7087806">https://doi.org/10.5281/zenodo.7087806</a> (Demir et al., 2022a).</p> |
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| ISSN: | 1866-3508 1866-3516 |