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Wenchong Tian

Showing results (1-10 of 18) with videos related to

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Water Research|December 23, 2022
Towards coordinated and robust real-time control: a decentralized approach for combined sewer overflow and urban flooding reduction based on multi-agent reinforcement learningZhiyu Zhang, Wenchong Tian, Zhenliang Liao
Environmental Science and Pollution Research International|August 15, 2019
Transfer learning for neural network model in chlorophyll-a dynamics predictionWenchong Tian, Zhenliang Liao, Xuan Wang
Environmental Science and Pollution Research International|February 27, 2021
Statistical comparison between SARIMA and ANN's performance for surface water quality time series predictionXuan Wang, Wenchong Tian, Zhenliang Liao
Journal of Environmental Management|May 9, 2020
Urban flood risk assessment and analysis with a 3D visualization method coupling the PP-PSO algorithm and building dataGuozheng Zhi, Zhenliang Liao, Wenchong Tian, et al.
Water Research|December 25, 2025
Differentiable neural network-based models enable gradient-based optimization for model predictive control of urban drainage networksZhiyu Zhang, Wenchong Tian, Zhenliang Liao, et al.
Water Research|July 15, 2026
Efficient reinforcement learning for urban drainage control via a neural network-based model using truncated and parallel rolloutsZhiyu Zhang, Wenchong Tian, Zhenliang Liao, et al.
Water Research|August 2, 2024
Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networksZhiyu Zhang, Wenchong Tian, Chenkaixiang Lu, et al.
Water Research|June 13, 2026
A graph-based frequency-domain model enables highly efficient modelling of sewer network hydraulicsShixun Li, Wenchong Tian, Zhiyu Zhang, et al.
Water Research|December 2, 2023
Improving the interpretability of deep reinforcement learning in urban drainage system operationWenchong Tian, Guangtao Fu, Kunlun Xin, et al.
Water Research|April 24, 2025
Enhancing the resilience of urban drainage system using deep reinforcement learningWenchong Tian, Zhiyu Zhang, Kunlun Xin, et al.
Pageof 2

Showing results (1-10 of 18) with videos related to

Sort By:
Pageof 2
Water Research|December 23, 2022
Towards coordinated and robust real-time control: a decentralized approach for combined sewer overflow and urban flooding reduction based on multi-agent reinforcement learningZhiyu Zhang, Wenchong Tian, Zhenliang Liao
Environmental Science and Pollution Research International|August 15, 2019
Transfer learning for neural network model in chlorophyll-a dynamics predictionWenchong Tian, Zhenliang Liao, Xuan Wang
Environmental Science and Pollution Research International|February 27, 2021
Statistical comparison between SARIMA and ANN's performance for surface water quality time series predictionXuan Wang, Wenchong Tian, Zhenliang Liao
Journal of Environmental Management|May 9, 2020
Urban flood risk assessment and analysis with a 3D visualization method coupling the PP-PSO algorithm and building dataGuozheng Zhi, Zhenliang Liao, Wenchong Tian, et al.
Water Research|December 25, 2025
Differentiable neural network-based models enable gradient-based optimization for model predictive control of urban drainage networksZhiyu Zhang, Wenchong Tian, Zhenliang Liao, et al.
Water Research|July 15, 2026
Efficient reinforcement learning for urban drainage control via a neural network-based model using truncated and parallel rolloutsZhiyu Zhang, Wenchong Tian, Zhenliang Liao, et al.
Water Research|August 2, 2024
Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networksZhiyu Zhang, Wenchong Tian, Chenkaixiang Lu, et al.
Water Research|June 13, 2026
A graph-based frequency-domain model enables highly efficient modelling of sewer network hydraulicsShixun Li, Wenchong Tian, Zhiyu Zhang, et al.
Water Research|December 2, 2023
Improving the interpretability of deep reinforcement learning in urban drainage system operationWenchong Tian, Guangtao Fu, Kunlun Xin, et al.
Water Research|April 24, 2025
Enhancing the resilience of urban drainage system using deep reinforcement learningWenchong Tian, Zhiyu Zhang, Kunlun Xin, et al.
Pageof 2