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The recurrent manifestation of anomalies in water supply network systems exerts a profound influence on individuals' daily lives. Despite this impact, contemporary research on urban water supply networks reveals a conspicuous lack in the thorough examination of spatiotemporal patterns and the relevance of these anomalies. This investigation meticulously scrutinizes anomalies within a specified segment of the water supply pipe network located in a county in southwest China. Clustering algorithms [ K -means and density-based spatial clustering of applications with noise (DBSCAN)] and statistical methods (standard deviation) identify anomalous water pressure. Subsequently, the Apriori algorithm is utilized to extract association rules for different types of anomalies, and these rules are compared with user similarity, quantified through standard Euclidean distance. The key findings are as follows. First, anomalies in water pressure are predominantly concentrated in May, September, and November. On a 24-h scale, the highest incidence of anomalies occurs between 6:00 a.m. and 9:00 a.m. Areas with the highest anomaly occurrence are primarily situated near the city center and the railway station. Second, correlation rules exist among occurrences of anomalous values at various monitoring sites within the study area. In concrete terms, identical water pressure abnormal types frequently co-occur (confidence level >50% , support level >3%) at diverse monitoring sites, with this correlation linked to the types of users around the monitoring sites. Finally, the categorization of anomalies results in significantly enhanced accuracy in correlation rule outcomes, surpassing the comprehensive analysis of anomalies overall. [ABSTRACT FROM AUTHOR]

Flash flooding is a type of global devastating hydrometeorological disaster that seriously threatens people's property and physical safety, as well as the normal operation of water conservancy facilities, such as reservoirs, so an accurate assessment of reservoir risk for certain areas is necessary. Therefore, the purpose of this study was to propose a novel methodological approach for reservoir risk modelling based on the feature selection method (FSM) and tree-based ensemble methods (Bagging and Random Forest [RF]). The results showed that: (1) the J48-GA based ensemble models achieved higher learning and predictive capabilities compared to conventional ensemble models without the FSM. (2) For the classification accuracy, the J48-GA-RF (96.4%) outperformed RF (96.0%), J48-GA-Bagging (93.9%) and Bagging (93.5%). And the J48-GA-RF achieved the highest prediction AUC value (0.995), an almost perfect Kappa indexes value (0.926) and the best practicality value (30.88%). (3) In particular, the results indicated that all of the models showed high performance, both in training and in the validation of a dataset. Additionally, this study could provide a reference for disaster managers, hydraulic engineers and policy makers to implement location-specific flash flood risk reduction strategies. [ABSTRACT FROM AUTHOR]

The vegetation carbon uptake plays an important role in the terrestrial carbon cycle on the Qinghai-Tibet Plateau (QTP), while it is extremely sensitive to the impact of natural external forcings. Until now, there is limited knowledge on the spatial-temporal patterns of vegetation net carbon uptake (VNCU) after the force that caused by tropical volcanic eruptions. Here, we conducted an exhaustive reconstruction of VNCU on the QTP over the last millennium, and used a superposed epoch analysis to characterize the VNCU response of the QTP after the tropical volcanic eruptions. We then further investigated the divergent changes of VNCU response across different elevation gradients and vegetation types, and the impact of teleconnection forcing on VNCU after volcanic eruptions. Within a climatic background, we found that VNCU of the QTP tends to decrease after large volcanic eruptions, lasting until about 3 years, with a maximum decrease value occurring in the following 1 year. The spatial and temporal patterns of the VNCU were mainly driven by the post-eruption climate and moderated by the negative phase trends of El Niño-Southern Oscillation and the Atlantic multidecadal oscillation. In addition, elevation and vegetation types were undeniable driving forces associated with VNCU on QTP. Different water-heat conditions and vegetation types contributed to significant differences in the response and recovery processes of VNCU. Our results emphasized the response and recovery processes of VNCU to volcanic eruptions without the strong anthropogenic forcings, while the influence mechanisms of natural forcing on VNCU should receive more attention., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Yong, Wang, Xiong and Tian.)