Articles | Volume 28, issue 16
https://doi.org/10.5194/hess-28-3739-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-3739-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Robust multi-objective optimization under multiple uncertainties using the CM-ROPAR approach: case study of water resources allocation in the Huaihe River basin
Jitao Zhang
College of Hydrology and water resources, Hohai University, Nanjing, 210000, China
Water Resources Section, Delft University of Technology, 2628 CD Delft, the Netherlands
IHE Delft Institute for Water Education, 2628 AX Delft, the Netherlands
Dimitri Solomatine
Water Resources Section, Delft University of Technology, 2628 CD Delft, the Netherlands
IHE Delft Institute for Water Education, 2628 AX Delft, the Netherlands
Water Problems Institute of RAS, 119333 Moscow, Russia
Zengchuan Dong
CORRESPONDING AUTHOR
College of Hydrology and water resources, Hohai University, Nanjing, 210000, China
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Ana Paez-Trujilo, Jeffer Cañon, Beatriz Hernandez, Gerald Corzo, and Dimitri Solomatine
Nat. Hazards Earth Syst. Sci., 23, 3863–3883, https://doi.org/10.5194/nhess-23-3863-2023, https://doi.org/10.5194/nhess-23-3863-2023, 2023
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This study uses a machine learning technique, the multivariate regression tree approach, to assess the hydroclimatic characteristics that govern agricultural and hydrological drought severity. The results show that the employed technique successfully identified the primary drivers of droughts and their critical thresholds. In addition, it provides relevant information to identify the areas most vulnerable to droughts and design strategies and interventions for drought management.
Mohamed Elneel Elshaikh Eltayeb Elbasheer, Gerald Augusto Corzo, Dimitri Solomatine, and Emmanouil Varouchakis
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-98, https://doi.org/10.5194/hess-2023-98, 2023
Revised manuscript not accepted
Short summary
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In this research, we explored the use of machine learning (ML) to improve the S2S ensemble precipitation forecast, different approaches were used as exploratory experiments to see which approach is better addressing the improvement of the ensemble probabilistic forecast, as a conclusion of our research, we found that the concept of committee model (CM) is a promising approach that can be further studied and evaluated using a different combination of the state of the art ML techniques.
Mohamed Elneel Elshaikh Eltayeb Elbasheer, Gerald Augusto Corzo, Dimitri Solomatine, and Emmanouil Varouchakis
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-348, https://doi.org/10.5194/hess-2022-348, 2022
Manuscript not accepted for further review
Short summary
Short summary
In this research, we explored the use of machine learning (ML) to improve the ECMWF S2S ensemble precipitation forecast, different approaches were used as exploratory experiments to see which approach is better addressing the improvement of the ensemble probabilistic forecast, as a conclusion of our research, we found that the concept of committee model (CM) is a promising approach that can be further studied and evaluated using a different combination of the state of the art ML techniques.
Vitali Diaz, Ahmed A. A. Osman, Gerald A. Corzo Perez, Henny A. J. Van Lanen, Shreedhar Maskey, and Dimitri Solomatine
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-252, https://doi.org/10.5194/hess-2022-252, 2022
Preprint withdrawn
Short summary
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Drought impacts on crops can be assessed in terms of crop yield (CY) variation. The hypothesis is that the spatiotemporal change of drought area is a good input to predict CY. A step-by-step approach for predicting CY is built based on two types of machine learning models. Drought area was found suitable for predicting CY. Since it is currently possible to calculate drought areas within drought monitoring systems, the prediction of drought impacts can be integrated directly into them.
Yaogeng Tan, Zengchuan Dong, Sandra M. Guzman, Xinkui Wang, and Wei Yan
Hydrol. Earth Syst. Sci., 25, 6495–6522, https://doi.org/10.5194/hess-25-6495-2021, https://doi.org/10.5194/hess-25-6495-2021, 2021
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The rapid increase in economic development and urbanization is contributing to the imbalances and conflicts between water supply and demand and further deteriorates river ecological health, which intensifies their interactions and causes water unsustainability. This paper proposes a methodology for sustainable development of water resources, considering socioeconomic development, food safety, and ecological protection, and the dynamic interactions across those water users are further assessed.
Vitali Diaz, Ahmed A. A. Osman, Gerald A. Corzo Perez, Henny A. J. Van Lanen, Shreedhar Maskey, and Dimitri Solomatine
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-600, https://doi.org/10.5194/hess-2021-600, 2021
Preprint withdrawn
Short summary
Short summary
Drought effects on crops are usually evaluated through crop yield (CY). The hypothesis is that the drought spatial extent is a good input to predict CY. A machine learning approach to predict crop yield is introduced. The use of drought area was found suitable. Since it is currently possible to calculate drought areas within drought monitoring systems, the direct application to predict drought effects can be integrated into them by following approaches such as the one presented or similar.
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Short summary
Faced with the problem of uncertainty in the field of water resources management, this paper proposes the Copula Multi-objective Robust Optimization and Probabilistic Analysis of Robustness (CM-ROPAR) approach to obtain robust water allocation schemes based on the uncertainty of drought and wet encounters and the uncertainty of inflow. We believe that this research article not only highlights the significance of the CM-ROPAR approach but also provides a new concept for uncertainty analysis.
Faced with the problem of uncertainty in the field of water resources management, this paper...