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Dam failure can cause extraordinary flooding. Water and wastewater treatment plants, electricity generation facilities, bridges, highways, population centers, and other dams often lie downstream of a dam. If poorly maintained, aging dams can be overtopped by extreme rainfall events, and long-term loss of critical infrastructure services for many communities and regions may result. Such events are now more likely as the climate changes. How should we prioritize dams that need immediate attention and retrofit or remove them? Losses from a dam failure could exceed the infrastructure's replacement costs, through cascading effects in multiple sectors such as electricity, transportation, water supply, and environmental services. The existing dam hazard classification frameworks in the US and many other countries do not formally characterize "hazard hotspots" considering these impacts. Given that there are over 90,000 dams at different stages of repair, maintenance, and budgetary constraints, a systematic approach is needed to rank their potential failure hazard and allocate resources for risk mitigation. In this paper, we synthesize an approach for preliminary ranking of the priority areas of concern. The approach is scalable over many regions for rapid assessment of magnitude and exposure of a dam failure. The estimation of the consequences of a dam failure, including its financial losses, as well as affected critical infrastructures and population, is explored using publicly available dam break and consequence tools as well as national infrastructure data sets. [ABSTRACT FROM AUTHOR]

This study proposes an improved nonstationary model for flood frequency analysis by investigating the relationship between flood peak and flood volume, using the Three Gorges Dam (TGD), China, for verification. First, the generalized additive model for location, scale and shape (GAMLSS) is used as the prior distribution. Then, under Bayesian theory, the prior distribution is updated using the conditional distribution, which is derived from the copula function. The results show that the improvement of the proposed model is significant compared with the GAMLSS-based prior distribution. Meanwhile, selection of a suitable prior distribution has a significant effect on the results of the improvement. For applications to the TGD, the nonstationary model can obviously increase the engineering management benefits and reduce the perceived risks of large floods. This study provides guidance for the dynamic management of hydraulic engineering under nonstationary conditions. [ABSTRACT FROM AUTHOR]

This narrative review paper explores the diverse applications of artificial intelligence (AI) in the field of dam engineering. Authored by research engineers specializing in civil engineering and data science, and reviewed by experienced dam engineers and AI experts, the paper aims to provide a comprehensive overview of the subject. The paper is structured into three parts. Part 1 offers a concise introduction to AI, covering its historical background, major categories, and current state of development. In Part 2, the focus shifts to the specific applications of AI in hydropower and dam engineering. This section delves into the utilization of AI for predictive modeling, real-time monitoring, optimization, and planning and design. Notable case studies and examples of machine learning techniques applied to predictive models and numerical simulations in the context of dams and levees are also discussed. Part 3 explores the current and emerging technologies being implemented in the industry, including automated decision-making systems and the use of AI-powered drones for efficient dam inspection. Additionally, the section sheds light on the challenges that must be addressed to fully integrate AI into dam engineering practices. This paper targets dam engineers, AI experts, and individuals interested in the intersection of these fields. Its primary contribution lies in providing a comprehensive and up-to-date review of the applications, challenges, and potential future developments of AI in dam engineering. Over 3300 articles on the intersection of dam engineering and AI were identified, and approximately 550 articles (about 17%) were further processed for inclusion in this study. Moreover, it emphasizes the need for robust data management, algorithmic transparency, and ethical considerations in the implementation of AI systems. [ABSTRACT FROM AUTHOR]