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WaterBench-Iowa: a large-scale benchmark dataset for data-driven streamflow forecasting
Journal article   Open access   Peer reviewed

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

Ibrahim Demir, Zhongrun Xiang, Bekir Demiray and Muhammed Sit
Earth system science data, Vol.14(12), pp.5605-5616
01/01/2022
DOI: 10.5194/essd-14-5605-2022
url
https://doi.org/10.5194/essd-14-5605-2022View
Published (Version of record) Open Access

Abstract

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 10.5281/zenodo.7087806 (Demir et al., 2022a).
 Earth Science Hydrology Machine Learning Mathematical Models Watersheds Benchmarks Comparative analysis Comparative studies Datasets Deep learning Earth science research Evapotranspiration Flood forecasting Floods Forecasting Government Learning algorithms Neural networks Regression analysis Runoff Scientists Sequencing Short term memory Soil types Spatial discrimination Spatial resolution Stream discharge Stream flow Streamflow forecasting Subject specialists Water runoff

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