Article
Engineering, Civil
Xinting Yu, Yue-Ping Xu, Haiting Gu, Yuxue Guo
Summary: This study developed a multi-objective robust optimization methodology for real-time reservoir flood control operation, which incorporated forecast uncertainty. Three machine learning models were used to forecast short-term reservoir inflow, and a stacking ensemble model was applied to integrate the forecasting results. The findings showed improved flood risk reduction and reservoir utilization.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Civil
Qingwen Lu, Ping-an Zhong, Bin Xu, Xin Huang, Feilin Zhu, Han Wang, Yufei Ma
Summary: This study analyzes the spatial correlation of reservoir initial water level errors and the spatiotemporal correlation of flood forecast errors using the copula function. It establishes a risk analysis model for multi-objective flood control operation of a complex reservoir system, considering multiple uncertainties. The study finds that neglecting the correlations between errors underestimates the flood control risk. There is a competitive tradeoff between upstream and downstream objectives, and choosing a compromise solution balances the risks.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Civil
Jieyu Li, Ping-an Zhong, Yuanjian Wang, Minzhi Yang, Jisi Fu, Weifeng Liu, Bin Xu
Summary: This paper evaluates the reliability of the multi-reservoir real-time flood control hybrid operation model in real-time flood control by conducting a risk analysis, using the case study of the Huaihe River basin to analyze the impact of model structure reduction on flood risk and related factors. The results indicate that reducing the model structure does not significantly increase flood risk, but considering the uncertainties in flood forecasting and model structure may lead to 65% increased risk.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Multidisciplinary
Aijun Guo, Jianxia Chang, Yimin Wang, Bin Wu, Yunyun Li
Summary: Increasing studies have shown that the uncertainty in design flood hydrograph (DFH) can impact flood management decisions. A novel methodological framework is proposed to trace the DFH uncertainty propagation in reservoir flood control systems. Results indicate that uncertainty in reservoir flood control operations is reduced by the optimal operation model, with less uncertainty near peak flow periods.
APPLIED MATHEMATICAL MODELLING
(2022)
Article
Engineering, Civil
Wei Ding, Guozhen Wei, Huicheng Zhou
Summary: This study introduces the concept of resilience to evaluate the ability of flood control systems to recover after extreme floods. A quantifiable measure of resilience is proposed, and a multi-objective optimization model is established to improve resilience and reduce flood risk. The results show that resilience can be improved without increasing flood risk by pre-releasing water and releasing slowly during the flood.
JOURNAL OF HYDROLOGY
(2023)
Article
Environmental Sciences
Wenhua Wan, Xiaohui Lei, Jianshi Zhao, Mingna Wang, Soon-Thiam Khu, Chao Wang
Summary: The traditional approach of using historical-record-based flood-limited water levels for reservoir flood control operations may not be suitable due to the non-stationarity of rainfall inputs. This study proposes a dynamic pre-storm level approach based on forecast accuracy to maximize floodwater utilization and reservoir storage without adding flood risk. Coupling this new approach with traditional flood-limited water levels allows for more efficient and economic day-to-day reservoir operations, considering improvements in flood forecast accuracy.
Article
Engineering, Civil
Jingwen Zhang, Ximing Cai, Xiaohui Lei, Pan Liu, Hao Wang
Summary: Real-time Optimization Model Enhanced by Data Assimilation (ROMEDA) is proposed as a human-machine interactive method for optimizing reservoir operations. Through a case study, it is found that ROMEDA shows better performance on flood risk mitigation and water use benefit, making it a promising approach for real-time reservoir operation integrating human expertise and computational modeling.
JOURNAL OF HYDROLOGY
(2021)
Article
Engineering, Civil
Hai-tao Chen, Wen-chuan Wang, Kwok-wing Chau, Lei Xu, Ji He
Summary: The study focused on the complex optimization problem of flood control operation (FCO) of a reservoir, establishing a reservoir group FCO model and developing a flood forecast model. Results from a case study showed that optimal operation can efficiently reduce storage capacity and lower flood peaks.
WATER RESOURCES MANAGEMENT
(2021)
Article
Environmental Sciences
Mengxia Zhao, Yanyi Liu, Ying Wang, Yu Chen, Wenfeng Ding
Summary: This paper examines the inundation extents of flash floods in the Wangmo River Basin in China, and finds that dam and reservoir operations can effectively reduce housing losses and generate economic benefits in the long run. The construction of dams and reservoirs is crucial for improving disaster resilience in poor mountainous areas.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Water Resources
Chengjing Xu, Ping-an Zhong, Feilin Zhu, Lingjie Li, Qingwen Lu, Luhua Yang
Summary: This article proposes a novel SMAA-VIKOR model for multi-criteria decision making in reservoir flood control operation. The model integrates stochastic multicriteria acceptability analysis theory with the Viekriterijumsko Kompromisno Rangiranje method. The results of a case study in the Dadu River in China demonstrate the model's ability to identify optimal alternatives and provide theoretical support for robust reservoir release decisions.
HYDROLOGICAL SCIENCES JOURNAL
(2023)
Article
Environmental Sciences
Minglei Ren, Qi Zhang, Yuxia Yang, Gang Wang, Wei Xu, Liping Zhao
Summary: This paper takes the Foziling Reservoir as an example and uses an improved genetic algorithm to optimize flood control dispatching during the flood process. The results show that the improved genetic algorithm saves time in determining penalty coefficients and provides a more convenient model application. The scheduling results obtained by the improved genetic algorithm can maximize the flood control capacity while ensuring the safety of the reservoir and downstream area.
Article
Engineering, Civil
Manizhe Zarei, Omid Bozorg-Haddad, Hugo A. Loaiciga
Summary: Floods cause financial and human losses globally, making it crucial to assess the effectiveness of flood management policies. This study introduces a new forensic engineering framework to evaluate the role of reservoir operation in achieving objectives during floods. The framework includes the development of two approaches, the standard operation policy (SOP) and ideal approach (IA), which are compared to historic management (HM). The results show that the SOP and IA outperform HM in terms of reservoir performance during flood events.
JOURNAL OF HYDROLOGY
(2023)
Article
Green & Sustainable Science & Technology
Ji He, Xiaoqi Guo, Haitao Chen, Fuxin Chai, Shengming Liu, Hongping Zhang, Wenbin Zang, Songlin Wang
Summary: This paper proposes a hybrid slime mold and arithmetic optimization algorithm (HSMAAOA) combining stochastic reverse learning to solve the complex engineering problem of joint flood control operation of reservoir groups. The algorithm is used to optimize the operation of five reservoirs in the middle and lower reaches of the Yellow River, and the results show that the HSMAAOA algorithm outperforms other algorithms with a peak clipping rate of 52.9%.
Article
Engineering, Civil
Chengguo Su, Peilin Wang, Wenlin Yuan, Chuntian Cheng, Taiheng Zhang, Denghua Yan, Zening Wu
Summary: This paper focuses on studying reservoir flood control operation with spillway gate scheduling, aiming to minimize the peak outflow and protect downstream areas from flood disasters. By linearizing nonlinear factors and converting the original model into a mixed-integer linear programming formulation, the proposed model is proven to be computationally efficient and capable of producing more realistic and executable flood control operation scheduling.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Civil
Chia -Wen Wu, Frederick N. -F. Chou
Summary: This study proposes a method to detect and remove systematic outliers in rainfall measurement using a conceptual rainfall-runoff model. It utilizes downstream observed reservoir inflow to supervise the detection and cleansing of upstream rain measurements. The method aims to remove outliers that significantly improve runoff simulation precision to meet the required accuracy. It can be used to purify historical rain data for hydrological studies and estimate current floodwater detained in the watershed for real-time flood management.
JOURNAL OF HYDROLOGY
(2023)
