Yingjian Zhou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yingjian Zhou has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Fuheng Ma
Nanjing Hydraulic Research Institute, Nanjing 210029, China
1 shared publication - Heqiang Sun
School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China
1 shared publication - Jianyou Wang
School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China
1 shared publication - Pieter H. A. J. M. van Gelder
Safety and Security Science Group (S3G), Faculty of Technology, Policy and Management, Delft University of Technology, 2628 BX Delft, The Netherlands
1 shared publication - Qing Shi
School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China
1 shared publication - Zongkun Li
School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China
1 shared publication
Latest Publications
- Open Access
Bayesian Network-Based Earth-Rock Dam Breach Probability Analysis Integrating Machine Learning
Earth-rock dams are critical components of hydraulic engineering, undertaking core functions such as flood control and disaster mitigation. However, the potential occurrence of dam breach poses a severe threat to regional socioeconomic stability and ecological security. To address the limitations of traditional Bayesian network (BN) in capturing the complex nonlinear coupling and dynamic mutual interactions among risk factors, they are integrated with machine learning techniques, based on a collected dataset of earth-rock dam breach case samples, the PC structure learning algorithm was employed to preliminarily uncover risk associations. The dataset was compiled from public databases, including the U.S. Army Corps of Engineers (USACE) and Dam Safety Management Center of the Ministry of Water Resources of China, as well as engineering reports from provincial water conservancy departments in China and Europe. Expert knowledge was integrated to optimize the network topology, thereby correcting causal relationships inconsistent with engineering mechanisms. The results indicate that the established hybrid model achieved AUC, accuracy, and F1-Score values of 0.887, 0.895, and 0.899, respectively, significantly outperforming the data-driven model G1. Forward inference identified the key drivers elevating breach risk. Conversely, backward inference revealed that overtopping was the direct failure mode with the highest probability of occurrence and the greatest contribution. The integration of data-driven approaches and domain knowledge provides theoretical and technical support for the probabilistic quantification of earth-rock dam breach and risk prevention and control decision-making.
Open AccessBayesian Network-Based Earth-Rock Dam Breach Probability Analysis Integrating Machine Learning
Earth-rock dams are critical components of hydraulic engineering, undertaking core functions such as flood control and disaster mitigation. However, the potential occurrence of dam breach poses a severe threat to regional socioeconomic stability and ecological security. To address the limitations of traditional Bayesian network (BN) in capturing the complex nonlinear coupling and dynamic mutual interactions among risk factors, they are integrated with machine learning techniques, based on a collected dataset of earth-rock dam breach case samples, the PC structure learning algorithm was employed to preliminarily uncover risk associations. The dataset was compiled from public databases, including the U.S. Army Corps of Engineers (USACE) and Dam Safety Management Center of the Ministry of Water Resources of China, as well as engineering reports from provincial water conservancy departments in China and Europe. Expert knowledge was integrated to optimize the network topology, thereby correcting causal relationships inconsistent with engineering mechanisms. The results indicate that the established hybrid model achieved AUC, accuracy, and F1-Score values of 0.887, 0.895, and 0.899, respectively, significantly outperforming the data-driven model G1. Forward inference identified the key drivers elevating breach risk. Conversely, backward inference revealed that overtopping was the direct failure mode with the highest probability of occurrence and the greatest contribution. The integration of data-driven approaches and domain knowledge provides theoretical and technical support for the probabilistic quantification of earth-rock dam breach and risk prevention and control decision-making.