Structural health monitoring is an effective method to evaluate the safety status of dams. Measurement error is an important factor which affects the accuracy of monitoring data modeling. Processing the abnormal monitoring data before data analysis is a necessary step to ensure the reliability of the analysis. In this paper, we proposed a method to process the abnormal dam displacement monitoring data on the basis of matrix manipulation and Cuckoo Search algorithm. We first generate a scatter plot of the monitoring data and exported the matrix of the image. The scatter plot of monitoring data includes isolate outliers, clusters of outliers, and clusters of normal points. The gray scales of isolated outliers are reduced using Gaussian blur. Then, the isolated outliers are eliminated using Ostu binarization. We then use the Cuckoo Search algorithm to distinguish the clusters of outliers and clusters of normal points to identify the process line. To evaluate the performance of the proposed data processing method, we also fitted the data processed by the proposed method and by the commonly used 3-$\sigma$ method using a regression model, respectively. Results indicate that the proposed method has a better performance in abnormal detection compared with the 3-$\sigma$ method.

The optimization of urban multi-source water supply systems is essential for addressing the growing challenges of water allocation, cost management, and system resilience in modern cities. This study introduces a graph-theory-based optimization model to analyze the structural and operational dynamics of urban water supply systems, incorporating constraints such as water quality, pressure, and system connectivity. Using Lishui City as a case study, the model evaluates three water allocation plans to meet the projected 2030 water demand. Advanced algorithms, including Floyd’s shortest path algorithm and the GA-COA-SA hybrid optimization algorithm, were employed to address constraints such as pipeline pressure, water quality attenuation, and nonlinear flow dynamics. Results indicate a 1.4% improvement in cost-effectiveness compared to the current allocation strategy, highlighting the model’s capability to enhance efficiency. Among the evaluated options, Plan 2 emerges as the most cost-effective solution, achieving a supply capacity of 4.5920 × 10⁵ m³/d with the lowest annual cost of 5.7015 × 10⁷ yuan, highlighting the model’s capability to improve both efficiency and resilience. This study prioritizes cost-efficiency tailored to regional challenges, distinguishing itself from prior research that emphasized redundancy and water quality analysis. The findings demonstrate the potential of graph-theoretic approaches combined with advanced optimization techniques to enhance decision-making for sustainable urban water management.

Slope deformation poses significant risks to infrastructure, ecosystems, and human safety, making early and accurate predictions essential for mitigating slope failures and landslides. In this study, we propose a novel approach that integrates a fractional-order grey model (FOGM) with particle swarm optimization (PSO) to determine the optimal fractional order, thereby enhancing the model’s accuracy, even with limited and fluctuating data. Additionally, we employ a k-means clustering technique to account for both temporal and spatial variations in multi-point monitoring data, which improves the model’s ability to capture the relationships between monitoring points and increases prediction relevance. The model was validated using displacement data collected from 12 monitoring points on a slope located in Qinghai Province near the Yellow River, China. The results demonstrate that the proposed model outperforms the traditional statistical model and artificial neural networks, achieving a significantly higher coefficient of determination $R^2$ up to 0.9998 for some monitoring points. Our findings highlight that the model maintains robust performance even when confronted with data of varying quality—a notable advantage over conventional approaches that typically struggle under such conditions. Overall, the proposed model offers a robust and data-efficient solution for slope deformation prediction, providing substantial potential for early warning systems and risk management.

For the prediction of landslide-generated waves, previous studies have developed numerous empirical equations to express the maximums of wave characteristics as functions of slide parameters upon impact. In this study, we built the temporal relationship between the wave characteristics and slide features. We gave specific insights into impulse waves generated by snow avalanches and mimicked them using a buoyant material called Carbopol whose density is close to that of water. Using the particle image velocimetry (PIV) technique, the slide’s temporal velocity field and thickness, as well as the temporal free water surface fluctuation, were determined experimentally. Using a statistical method denoted as panel data analysis, we quantified the temporal wave amplitude from the time series data of the thickness and depth-averaged velocity of the sliding mass at the shoreline. Then, the slide’s temporal thickness and velocity at the shoreline were estimated from the parameters of the stationary slide at the initial position, based on the viscoplastic theory. Combining the panel data analysis and the viscoplastic theory, the temporal wave amplitudes were estimated from the initial slide parameters. In the end, we validated the proposed theoretical–statistical combined predictive method with the support of experimental data.

As an important working condition in water conveyance projects, the water filling process of pipelines is a complex hydraulic transition process involving water–air two-phase flow with sharp pressure changes that can easily cause pipeline damage. In light of the complex water–air two-phase flow during pipeline water filling, this study explores the water filling process of right-angle elbow pressure pipelines using CFD numerical simulations and physical model experiments, analyzing changes in water phase volume fraction, water-gas two-phase flow patterns, and hydraulic parameters in the pipeline under low flow rate conditions of 0.6 m/s and high flow rate conditions of 1.5 m/s. Results show that under low flow rate conditions, there is more local trapped gas at the top of the pipeline, causing negative pressure at local high points in the pipeline and forming a vacuum. Under high velocity conditions, water-gas two-phase flow changes more frequently in the pipeline, with a large number of bubbles collapsing at the top, resulting in large fluctuations in pipeline pressure. Finally, through physical experiments, the main flow patterns during water filling in right-angle elbows are verified and analyzed. These results have certain reference significance for formulating safe and efficient water filling velocity schemes for pressurized pipelines.

Considering the issues of water scarcity, water environment deterioration, and unreasonable allocation of water resources in the urban area of the Yellow River Basin, this paper introduces the human–water harmony theory to the allocation of regional water resources. Based on an analysis of the structural characteristics of the regional water resource system, the harmonious water resource allocation (HWRA) model—which includes three sub-systems (i.e., the water service system, ecological environmental system, and economic and social system)—is established. In addition, considering the uncertain factors in the HWRA model, the inexact fuzzy multi-objective programming (IFMOP) method is used to solve the model, aiming at achieving the minimum amount of regional water scarcity, the minimum amount of sewage discharge, and the maximum total economic benefit. A case study of water resource allocation of Binzhou, a city located in the Yellow River Basin, is conducted to validate the model. The model solution results show that the water resource system in Binzhou in 2025 and 2035 could be optimized after harmonious allocation, especially in terms of the water service and ecological environmental systems. Compared with the optimal water resource allocation (OWRA) model, the HWRA model has a more scientific water supply structure, and a smaller amount of sewage discharge. The HWRA model solves the variables using an interval number, so it can flexibly and scientifically reflect the decision-making process.