On the use of machine learning to account for reservoir management rules and predict streamflow

Tounsi, Achraf; Temimi, Marouane; Gourley, Jonathan J.

doi:10.1007/s00521-022-07500-1

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On the use of machine learning to account for reservoir management rules and predict streamflow

2022
By Tounsi, Achraf ; Temimi, Marouane ; Gourley, Jonathan J.
Source: Neural Computing and Applications, 34(21), 18917-18931

[PDF-4.24 MB]

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Journal Title:

Neural Computing and Applications
Personal Author:

Tounsi, Achraf ; Temimi, Marouane ; Gourley, Jonathan J.

Tounsi, Achraf ; Temimi, Marouane ; Gourley, Jonathan J. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory)

OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) Less -
Description:

This study aims to develop a Machine Learning (ML)-based technique to infer reservoir management rules and predict downstream discharge values. The case study is the Hackensack River Watershed in New Jersey, USA. A Long Short-Term Memory (LSTM) model was used to predict streamflow values at the USGS station at New Milford, right downstream of Oradell reservoir. A good agreement between observed and simulated streamflow values was obtained during the 2020–2021 testing period. An NSE value of 0.93 was determined with the 48-h precipitation lead time, suggesting that the 48-h precipitation forecast mostly drives releases Oradell reservoir. The developed model was tested during Hurricane Ida. The analysis revealed that a similar NSE of 0.95 was obtained with a 48-h precipitation lead time followed by the 12-h lead time model, which was based on the watershed response time. In addition, the conducted feature analysis revealed that only four out of the seven upstream USGS stations in the watershed have a significant impact on the model’s performance. This work implies that ML can capture reservoir management rules and predict reservoir releases using precipitation and upstream flow data as input variables. This study lays the groundwork for a generalization of the method over the CONUS to infer reservoirs’ operation rules for streamflow simulation.
Keywords:

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Machine Learning Precipitation (Meteorology) Streamflow Artificial Intelligence LSTM MRMS Precipitation Software Water Reservoir
Source:

Neural Computing and Applications, 34(21), 18917-18931
DOI:

https://doi.org/10.1007/s00521-022-07500-1
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Journal Article
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urn:sha256:decaa5529ff4e56fe97a32fad26b0bdf1689b67b1624cf02a1ae78d13e08f249
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On the use of machine learning to account for reservoir management rules and predict streamflow

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