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Hexiang Yan

Showing results (11-20 of 24) with videos related to

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The Science of the Total Environment|May 18, 2019
A simple but robust convergence trajectory controlled method for pressure driven analysis in water distribution systemHexiang Yan, Qiongyu Wang, Jiaying Wang, et al.
Water Science and Technology : a Journal of the International Association on Water Pollution Research|September 15, 2025
Enhancing drainage pipeline inspection: advanced integration of six-axis sensor fusion and extended Kalman filterJinheng Meng, Yunlong Xiao, Hexiang Yan, et al.
Water Research X|November 5, 2024
Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learningShixun Li, Wenchong Tian, Hexiang Yan, et al.
Water Research|October 17, 2024
Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learningSiyi Wang, Jiaying Wang, Kunlun Xin, et al.
Environmental Science and Pollution Research International|March 2, 2023
GIS-based fuzzy comprehensive evaluation of urban flooding risk with socioeconomic index system developmentFei Li, Jieru Yan, Xiaolan Xiong, et al.
Water Research|June 15, 2025
Deep reinforcement learning control as an innovative approach for urban drainage systems: review and prospectsZichen He, Wenchong Tian, Jiaying Wang, et al.
Water Science and Technology : a Journal of the International Association on Water Pollution Research|June 15, 2024
Urban flood modeling with a novel coupling method of surface and sewer hydrodynamic processesSiyi Wang, Jiaying Wang, Kunlun Xin, et al.
The Science of the Total Environment|July 16, 2019
Laboratory analysis on the surface runoff pollution reduction performance of permeable pavementsJia Liu, Hexiang Yan, Ziyuan Liao, et al.
Journal of Environmental Management|February 28, 2023
Optimal sensor placement for the routine monitoring of urban drainage systems: A re-clustering methodSiyi Wang, Xiangwei Zhang, Jiaying Wang, et al.
Water Research|November 9, 2025
A knowledge-data fusion framework accelerates deep reinforcement learning for real-time control of urban drainage systemsWenchong Tian, Zhiyu Zhang, Xuan Wang, et al.
Pageof 3

Showing results (11-20 of 24) with videos related to

Sort By:
Pageof 3
The Science of the Total Environment|May 18, 2019
A simple but robust convergence trajectory controlled method for pressure driven analysis in water distribution systemHexiang Yan, Qiongyu Wang, Jiaying Wang, et al.
Water Science and Technology : a Journal of the International Association on Water Pollution Research|September 15, 2025
Enhancing drainage pipeline inspection: advanced integration of six-axis sensor fusion and extended Kalman filterJinheng Meng, Yunlong Xiao, Hexiang Yan, et al.
Water Research X|November 5, 2024
Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learningShixun Li, Wenchong Tian, Hexiang Yan, et al.
Water Research|October 17, 2024
Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learningSiyi Wang, Jiaying Wang, Kunlun Xin, et al.
Environmental Science and Pollution Research International|March 2, 2023
GIS-based fuzzy comprehensive evaluation of urban flooding risk with socioeconomic index system developmentFei Li, Jieru Yan, Xiaolan Xiong, et al.
Water Research|June 15, 2025
Deep reinforcement learning control as an innovative approach for urban drainage systems: review and prospectsZichen He, Wenchong Tian, Jiaying Wang, et al.
Water Science and Technology : a Journal of the International Association on Water Pollution Research|June 15, 2024
Urban flood modeling with a novel coupling method of surface and sewer hydrodynamic processesSiyi Wang, Jiaying Wang, Kunlun Xin, et al.
The Science of the Total Environment|July 16, 2019
Laboratory analysis on the surface runoff pollution reduction performance of permeable pavementsJia Liu, Hexiang Yan, Ziyuan Liao, et al.
Journal of Environmental Management|February 28, 2023
Optimal sensor placement for the routine monitoring of urban drainage systems: A re-clustering methodSiyi Wang, Xiangwei Zhang, Jiaying Wang, et al.
Water Research|November 9, 2025
A knowledge-data fusion framework accelerates deep reinforcement learning for real-time control of urban drainage systemsWenchong Tian, Zhiyu Zhang, Xuan Wang, et al.
Pageof 3