Due to global warming, considerable amounts of storm rain have occurred, causing urban water logging and flooding. The efficient scheduling of drainage systems among pumping stations is crucial to mitigating flash flooding in urban areas. This study introduces a Multi-Level Dynamic Priority and Importance Scheduling (MDPIS) algorithm as a proactive solution for addressing urban flooding through the optimization of drainage system discharge capacities. The algorithm's robustness is guaranteed through the integration of a multi-tier drainage system and dependency relationships. Additionally, the incorporation of an importance parameter is considered for facilitating the practical exploration of flooding risk evaluation. The proposed model was applied to simulate a drainage system in Haining City, and the results indicate that its accuracy, flexibility and reliability outperform that of existing algorithms such as fixed-priority scheduling. Moreover, the proposed approach enabled a considerable reduction in overflow loss and improved the efficiency of the sewage system. This method can improve the responses of cities to the rising problem of urban water logging.

China’s chemical manufacturing sector has experienced remarkable growth in recent years, making it a global leader in chemical production. However, this rapid expansion has led to an increase in chemical accidents, particularly major chemical accidents (MCAs), resulting in significant casualties and property loss. This study focuses on MCAs that occurred in China between 2017 and 2022, using mathematical statistics. It examines various aspects of accidents, including the annual number of accidents and casualties, distribution across months, types of accidents, accident stages, direct causes of accidents, and geographical distribution of accidents. Furthermore, this study investigated the potential of using novel digital tools to enhance the safety of chemical production. By analysing the data and identifying trends, this study aims to contribute to the prevention of large-scale chemical disasters. Furthermore, it explores the implementation of smart management of chemical plants, utilising the Internet of Things (IoT) for example, to ensure the sustainable advancement of the chemical industry.

Geological detection ahead of shield cutterhead is a challenge in the construction of large-diameter shield tunnels. This study proposed a framework for geological conditions identification based on stacking algorithm in large-diameter shield tunnel excavation of Chunfeng tunnel project. The relationships among shield parameters were analyzed using the Spearman rank correlation coefficient. Principal component analysis was employed to extract essential information from the parameters during shield tunneling. Then, the K-means + + clustering algorithm was used to construct a correlation dataset between the principal components of shield parameters and the categories of geological conditions. Subsequently, the Stacking algorithm was applied to recognize the geological categories ahead of the shield cutterhead, and the results were compared with those of optimized random forest, support vector machine, and gradient boosting decision tree algorithms. The proposed method achieved superior performance, accurately classifying geological features ahead of the shield cutterhead with an accuracy of 0.984. Moreover, the stacking algorithm achieves higher accuracy in geological category recognition compared to the optimized single-machine learning algorithms. This research results can provide valuable geological conditions for adjusting shield parameters, gesture control, and risk reduction during the large-diameter shield tunneling.

Flood vulnerability assessment is a critical component of flood risk management, particularly in regions with complex river networks and limited hydrological data. This study proposes a graph neural network-based framework to evaluate river system vulnerability, representing each river segment as a node with hydrological and geomorphological attributes. Two GNN models were employed to generate vulnerability scores by jointly considering node attributes and network structure. High-risk river segments were first identified based on these scores, and the results were then aggregated to delineate flood-sensitive sub-basins. A case study in Guangxi, China, using the Xijiang River system, shows that the two models converge on similar high-risk areas, with an overlap of 60% in identified high-risk segments, aligning well with observed flood patterns. This highlights the robustness and practical reliability of the proposed approach. The framework offers a practical, data-efficient tool for identifying vulnerable river segments and flood-prone sub-basins, supporting flood risk management and decision-making in complex river systems.