图信号技术在供水管网中的应用
- ZHOU X, ZHANG J, GUO S, et al. 2023. A convenient and stable graph-based pressure estimation methodology for water distribution networks: Development and field validation. Water Research [J], 233: 119747.
- ZHOU X, WAN X, LIU S, et al. 2024. An all-purpose method for optimal pressure sensor placement in water distribution networks based on graph signal analysis. Water Research [J], 266: 122354.
- ZHOU X, MAN Y, LIU S, et al. 2024. Leveraging multi-level correlations for imputing monitoring data in water supply systems using graph signal sampling theory. Water Research X [J], 25: 100274.
机器学习技术在供水管网中的应用
- ZHOU X, TANG Z, XU W, et al. 2019. Deep learning identifies accurate burst locations in water distribution networks. Water Research [J], 166: 115058.
- YUAN R, SU K, MAN Y, et al. 2024. Industrial water withdrawal prediction using multi-head attention encoder model. AQUA - Water Infrastructure, Ecosystems and Society [J], 73: 1868-1883.
- 刘怀利, 徐浩, 曼亚灿, 等.皖南农村地区需水量特征分析及预测模型研究[J].给水排水,2024,60(03):17-24+31.DOI:10.13789/j.cnki.wwe1964.2023.07.21.0005.
供水管网水力模型构建及校核
- ZHOU X, XU W, XIN K, et al. 2018. Self-Adaptive Calibration of Real-Time Demand and Roughness of Water Distribution Systems. Water Resources Research [J], 54: 5536-5550.
- CHEN X, ZHOU X, XIN K, et al. 2022. Sensitivity-Oriented Clustering Method for Parameter Grouping in Water Network Model Calibration. Water Resources Research [J], 58: e2021WR031206.
- ZHOU X, GUO S, XIN K, et al. 2022. Maintaining the long-term accuracy of water distribution models with data assimilation methods: A comparative study. Water Research [J], 226: 119268.
供水管网漏损控制
- [ZHOU X, TANG Z, XU W, et al. 2019. Deep learning identifies accurate burst locations in water distribution networks. Water Research [J], 166: 115058.]
- 周啸,刘书明,王春艳.典型居民水表计量误差曲线拟合及性能分析[J].给水排水,2022,58(07):27-31.DOI:10.13789/j.cnki.wwe1964.2021.06.09.0003.
- 周啸,刘书明,张娟,等.供水管网漏损评定的二元水平衡分析理论探析[J].给水排水,2023,59(12):124-129.DOI:10.13789/j.cnki.wwe1964.2023.08.04.0001.