Zongkun Li

Traditional methods for assessing levee projects focus solely on single‐year economic data, evaluating only the reduction in flood‐related economic losses while neglecting the benefits of saving lives and the dynamic changes in the economy and population over the project's design life. This study introduces a dynamic evaluation method for flood control and waterlogging drainage benefits of levee projects, incorporating economic and demographic changes. The total benefits are categorized into economic and life‐saving components. Separate hydrodynamic models for flood routing in river channels and rainfall‐waterlogging models in the basin are established. Through comparative analysis (with and without projects), the number of lives saved and the economic benefits generated by flood control and waterlogging drainage can be estimated. The Value of Statistical Life (VSL) is used to monetize life benefits, and calculation methods for flood control and waterlogging drainage benefits that considers both economic and life benefits are proposed. Then, a dynamic model incorporating regional GDP growth rates and population change coefficients is constructed, which can quantify the above‐mentioned benefits over the project's lifespan. A case study demonstrates that annual benefits increase from CNY 10.6 million in the base year to CNY 34.4 million by the end of the design life, with flood control benefits accounting for 59.1% and drainage benefits for 40.9%. It provides a comprehensive tool to assess long‐term project value, supporting investment decisions and improving benefit assessment systems for disaster risk reduction in levee projects.

Ecological water supply has been integrated into the water supply scope of numerous water diversion projects, yet ecosystem complexity makes ecological benefit assessment difficult. Traditional quantitative methods for water supply benefits mainly focus on economic benefits from productive and domestic water use, ignoring the ecological benefits and real-time benefit changes over the project service period. This study categorizes ecological water supply separately in benefit calculation and considers enhanced ecosystem service value brought by ecological water replenishment. Based on emergy analysis, the water resource values of domestic, productive, and ecological water supply are calculated individually, and a water supply benefits evaluation model is established in combination with corresponding water supply volumes. A dynamic evaluation method for the project service period is further proposed using a Grey Model (1,1). Verified via the Yangtze-to-Huaihe Water Diversion Project (Henan section), results indicate that from 2021 to 2070, comprehensive water supply benefits increase yearly from 5.19 billion yuan to 16.18 billion yuan. Productive water supply provides the largest benefits (67–74%), followed by urban domestic (19–25%), rural domestic (5–7%), and ecological water supply (1–2%). This study constructs a comprehensive, systematic, and dynamic evaluation system, supporting the holistic assessment of the long-term economic and environmental impacts of water diversion projects.

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.

Probabilistic analysis of overtopping is an important aspect of dam construction, and the uncertainty in the construction progress complicates the calculation of overtopping probabilities. Construction progress is significantly influenced by human factors, making it difficult to assign an accurate probability distribution. Although the interval non-probabilistic reliability (INPR) method can estimate the likelihood of events with unknown probability distributions, its calculation results for the overtopping probability have significant errors. To improve the rationality of the results, we developed a method that adopted flood frequency of the upstream flood level to correct the traditional INPR calculations, developing an improved model. Taking the Qianping Reservoir as an example, the overtopping probabilities for three construction schedules were calculated, and the results were compared with a flood routing calculation. The results indicated that the improved approach significantly improves the rationality of the calculations, and the results effectively reflected the impact of construction progress uncertainty on overtopping probabilities.

High-grade highways are an important part of the modern comprehensive transportation system. However, due to frequent natural disasters, harsh meteorological conditions, and fragile geological environments, high-grade highway construction projects face significant risks, and how to specifically manage and control these construction risks to reduce them to a socially acceptable level has become a pressing technical issue. Therefore, this study combines the construction characteristics and risk features of high-grade highways, applies the Hall's threedimensional structural theory to comprehensively identify potential risk factors from the dimensions of time, structure, and logic, and builds the logical dimension from four aspects: people, materials, environment, and management. To filter the main influencing factors, the Delphi method is adopted to construct a risk assessment indicator system, with the expert opinions fully taken into consideration. To address the subjectivity in the weight calculation process of risk assessment indicators, the Analytic Hierarchy Process (AHP) and Entropy Weight Method are used to calculate the subjective and objective weights, respectively. A combined weighting model is established based on game theory principles and is used to optimize the weights of the risk assessment indicators. In view of the fuzziness of risks during high-grade highway construction, fuzzy mathematics theory is introduced to construct the risk assessment model. In this study, this method is applied to the construction of the Elsiyah Highway to clarify the risk level of the project and propose targeted control measures. The results show that the risk level of the Elsiyah Highway project is relatively high. The risk level is conditionally acceptable, but measures must be taken to reduce the risks.

The inundated area of dam and dike-break floods includes various types of land and factories that release considerable amounts of pollutants into floods, causing serious water pollution and further endangering human health. Many pollution sources and factors affect the water pollution risk in inundated areas. Accurate assessment of the water pollution risk for dam and dike-break floods enables people to take measures in advance to reduce public health problems. The existing evaluation methods cannot effectively analyze the water pollution risk for dam and dike-break floods because partial or all pollution sources and influencing factors are ignored. The main factors affecting flood water quality were summarized into point source (PS), non-point source (NPS), flood depth, velocity, duration, and temperature. The water pollution risk caused by NPSs and PSs were quantified, as well as the impact of all main factors on water pollution risk. The evaluation model proposed for water pollution risk in inundated areas of dam and dike-break floods considers all pollution sources and influencing factors. The WPR was proposed to represent the water pollution risk value. The dam-break flood of Luhun Reservoir was simulated to verify the feasibility of the evaluation model. We concluded that (1) WPR varied with space and time in the inundated area and was seriously affected by PS in local areas; (2) the annual average WPR of different land use types from high to low were construction land, cropland, urban, water, rural area, woodland, and grassland. The evaluation model can be used to evaluate the water pollution risk for dam and dike-break floods at macro and micro scales. People can use this method to evaluate the impact, range, and degree of specific pollution sources or pollutants in the inundated area, thus allowing for measures to be taken in advance to reduce associated damages.