Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modeling
Accurately simulating river discharge remains a challenge. Hybrid models combining hydrological models with machine learning improve discharge simulation and offer better interpretability than standalone machine learning models. However, the commonly used models are deterministic. This study introdu...
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| Veröffentlicht in: | Results in engineering Jg. 25; S. 104079 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.03.2025
Elsevier |
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| ISSN: | 2590-1230, 2590-1230 |
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| Abstract | Accurately simulating river discharge remains a challenge. Hybrid models combining hydrological models with machine learning improve discharge simulation and offer better interpretability than standalone machine learning models. However, the commonly used models are deterministic. This study introduces an innovative extension to stochastic hydrological models, offering a novel combination that has not been previously explored. The proposed approach predicts discharge by integrating the simulated statistical properties of daily discharge probability distributions, derived from a stochastic rainfall-runoff model, into machine learning frameworks. This integration allows the machine learning models to incorporate insights from the uncertainties in discharge, thereby enhancing predictive accuracy of discharge simulations. The hybridization presented combines the physically-based stochastic HyMoLAP (Sto. HyMoLAP) model with machine learning techniques, including Wavelet-based eXtreme Gradient Boosting (WXGBoost) and Wavelet-based Gated Recurrent Unit (WGRU). Evaluated on the Ouémé at Bonou river basin, Benin, the Sto. HyMoLAP-WGRU model shows the best predictive performance, especially for low and high discharges. It achieves an overall Nash-Sutcliffe Efficiency (NSE) of 0.896, which is 7.30% higher than the NSE of HyMoLAP, and 29.67% and 259.71% higher than those of the standalone machine learning models. The Combined Accuracy (CA) is 38.11, reflecting reductions of 19.81%, 42.30%, and 62.41% compared to the standalone models. The analyses show that the performance of hybrid models depends on the simulated discharge distribution properties used as input. They suggest that the hybridization approach could be particularly beneficial for runoff simulations in catchments subject to significant random fluctuations where point discharge simulation is challenging.
•A novel hybrid technique is developed using only precipitation and evapotranspiration to predict discharge.•Integrating uncertainty insights from stochastic models into machine learning enhances discharge predictions.•The Sto.HyMoLAP-WGRU hybrid model demonstrates the best predictive performance, particularly for low and high discharges.•The performance of the hybridization technique depends on the range of uncertainties considered. |
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| AbstractList | Accurately simulating river discharge remains a challenge. Hybrid models combining hydrological models with machine learning improve discharge simulation and offer better interpretability than standalone machine learning models. However, the commonly used models are deterministic. This study introduces an innovative extension to stochastic hydrological models, offering a novel combination that has not been previously explored. The proposed approach predicts discharge by integrating the simulated statistical properties of daily discharge probability distributions, derived from a stochastic rainfall-runoff model, into machine learning frameworks. This integration allows the machine learning models to incorporate insights from the uncertainties in discharge, thereby enhancing predictive accuracy of discharge simulations. The hybridization presented combines the physically-based stochastic HyMoLAP (Sto. HyMoLAP) model with machine learning techniques, including Wavelet-based eXtreme Gradient Boosting (WXGBoost) and Wavelet-based Gated Recurrent Unit (WGRU). Evaluated on the Ouémé at Bonou river basin, Benin, the Sto. HyMoLAP-WGRU model shows the best predictive performance, especially for low and high discharges. It achieves an overall Nash-Sutcliffe Efficiency (NSE) of 0.896, which is 7.30% higher than the NSE of HyMoLAP, and 29.67% and 259.71% higher than those of the standalone machine learning models. The Combined Accuracy (CA) is 38.11, reflecting reductions of 19.81%, 42.30%, and 62.41% compared to the standalone models. The analyses show that the performance of hybrid models depends on the simulated discharge distribution properties used as input. They suggest that the hybridization approach could be particularly beneficial for runoff simulations in catchments subject to significant random fluctuations where point discharge simulation is challenging.
•A novel hybrid technique is developed using only precipitation and evapotranspiration to predict discharge.•Integrating uncertainty insights from stochastic models into machine learning enhances discharge predictions.•The Sto.HyMoLAP-WGRU hybrid model demonstrates the best predictive performance, particularly for low and high discharges.•The performance of the hybridization technique depends on the range of uncertainties considered. Accurately simulating river discharge remains a challenge. Hybrid models combining hydrological models with machine learning improve discharge simulation and offer better interpretability than standalone machine learning models. However, the commonly used models are deterministic. This study introduces an innovative extension to stochastic hydrological models, offering a novel combination that has not been previously explored. The proposed approach predicts discharge by integrating the simulated statistical properties of daily discharge probability distributions, derived from a stochastic rainfall-runoff model, into machine learning frameworks. This integration allows the machine learning models to incorporate insights from the uncertainties in discharge, thereby enhancing predictive accuracy of discharge simulations. The hybridization presented combines the physically-based stochastic HyMoLAP (Sto. HyMoLAP) model with machine learning techniques, including Wavelet-based eXtreme Gradient Boosting (WXGBoost) and Wavelet-based Gated Recurrent Unit (WGRU). Evaluated on the Ouémé at Bonou river basin, Benin, the Sto. HyMoLAP-WGRU model shows the best predictive performance, especially for low and high discharges. It achieves an overall Nash-Sutcliffe Efficiency (NSE) of 0.896, which is 7.30% higher than the NSE of HyMoLAP, and 29.67% and 259.71% higher than those of the standalone machine learning models. The Combined Accuracy (CA) is 38.11, reflecting reductions of 19.81%, 42.30%, and 62.41% compared to the standalone models. The analyses show that the performance of hybrid models depends on the simulated discharge distribution properties used as input. They suggest that the hybridization approach could be particularly beneficial for runoff simulations in catchments subject to significant random fluctuations where point discharge simulation is challenging. |
| ArticleNumber | 104079 |
| Author | Johnson, Olatunji Ronoh, Erick K. Moore, Stephen E. Houénafa, Sianou Ezéckiel |
| Author_xml | – sequence: 1 givenname: Sianou Ezéckiel orcidid: 0009-0003-5156-0374 surname: Houénafa fullname: Houénafa, Sianou Ezéckiel email: ezeckiel.sianou@students.jkuat.ac.ke organization: Department of Mathematics, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya – sequence: 2 givenname: Olatunji surname: Johnson fullname: Johnson, Olatunji organization: Department of Mathematics, University of Manchester, Manchester, United Kingdom – sequence: 3 givenname: Erick K. surname: Ronoh fullname: Ronoh, Erick K. organization: Department of Agricultural and Biosystems Engineering, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya – sequence: 4 givenname: Stephen E. surname: Moore fullname: Moore, Stephen E. organization: Department of Mathematics, University of Cape Coast, Cape Coast, Ghana |
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