Distributed long-term hourly streamflow predictions using deep learning – A case study for State of Iowa

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.envsoft.2020.104761. This is version 2 of this Preprint.


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Zhongrun Xiang, Ibrahim Demir


Accurate streamflow forecasting has always been a challenge. Although there were many novel approaches using deep learning models, accuracy of these models is often limited to a short lead time. This study proposes a new deep recurrent neural network approach, Neural Runoff Model (NRM), which has been applied on 125 USGS streamflow gages in the State of Iowa for predicting the next 120 hours. We use a semi-distributed model structure with observation and forecast data from the model output of upstream stations as additional input for downstream gages. The proposed model outperforms the streamflow persistence, ridge regression and random forest regression on majority of the gages. Our model has shown strong predictive power and can be used for long-term streamflow predictions. This study also shows that the semi-distributed structure in NRM can improve the streamflow predictions by integrating water level data from upstream stream gauges.




Civil and Environmental Engineering, Civil Engineering, Engineering, Environmental Engineering


Deep learning, data integration modeling, distributed model, Rainfall-Runoff modeling, streamflow forecasting


Published: 2020-03-21 19:36

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GNU Lesser General Public License (LGPL) 2.1

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