A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy
Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reli...
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| Published in: | Water resources research Vol. 59; no. 2 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Washington
John Wiley & Sons, Inc
01.02.2023
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| ISSN: | 0043-1397, 1944-7973 |
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| Abstract | Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time.
Key Points
Hybrid hydrological model integrates process‐based model with machine learning algorithm through data assimilation technique
Dynamic error correction framework capable of improving the streamflow forecast at longer lead time is proposed
Overall the developed framework improves the forecast accuracy along with quantifying the model prediction uncertainty |
|---|---|
| AbstractList | Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time.
Hybrid hydrological model integrates process‐based model with machine learning algorithm through data assimilation technique
Dynamic error correction framework capable of improving the streamflow forecast at longer lead time is proposed
Overall the developed framework improves the forecast accuracy along with quantifying the model prediction uncertainty Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time. Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time. Key Points Hybrid hydrological model integrates process‐based model with machine learning algorithm through data assimilation technique Dynamic error correction framework capable of improving the streamflow forecast at longer lead time is proposed Overall the developed framework improves the forecast accuracy along with quantifying the model prediction uncertainty |
| Author | Adeloye, Adebayo J. Roy, Abhinanda Kasiviswanathan, K. S. Ojha, Chandra Shekhar P. Patidar, Sandhya Soundharajan, Bankaru‐Swamy |
| Author_xml | – sequence: 1 givenname: Abhinanda surname: Roy fullname: Roy, Abhinanda organization: Indian Institute of Technology Roorkee – sequence: 2 givenname: K. S. orcidid: 0000-0003-4706-6931 surname: Kasiviswanathan fullname: Kasiviswanathan, K. S. email: k.kasiviswanathan@wr.iitr.ac.in organization: Indian Institute of Technology Roorkee – sequence: 3 givenname: Sandhya orcidid: 0000-0001-9562-6986 surname: Patidar fullname: Patidar, Sandhya organization: Heriot‐Watt University – sequence: 4 givenname: Adebayo J. orcidid: 0000-0002-2820-4596 surname: Adeloye fullname: Adeloye, Adebayo J. organization: Heriot‐Watt University – sequence: 5 givenname: Bankaru‐Swamy orcidid: 0000-0001-6143-9293 surname: Soundharajan fullname: Soundharajan, Bankaru‐Swamy organization: Amrita Vishwa Vidyapeetham – sequence: 6 givenname: Chandra Shekhar P. surname: Ojha fullname: Ojha, Chandra Shekhar P. organization: Indian Institute of Technology Roorkee |
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| SubjectTerms | Accuracy Algorithms Climate change Environmental impact Error correction Error correction & detection Flood forecasting Flood warning systems Floods Forecast accuracy Frameworks HBV model hybrid model India Lead time Machine learning Mathematical models Model accuracy model validation Modelling Nepal particle filter Physics random forest River basins Rivers Stream discharge Stream flow streamflow forecast Streamflow forecasting Uncertainty uncertainty quantification Warning systems water watersheds |
| Title | A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy |
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