Zhiyu Zhang 张智宇

I received my Ph.D. in Environmental Engineering from City University of Hong Kong and my D.Eng. in Environmental Engineering from Tongji University, supervised by Prof. Zhiguo Yuan and Prof. Zhenliang Liao, respectively. During my doctoral studies, I was a member of the Smart Water and EnviroBiotech Team (SWEB) at CityUHK. I received my B.Eng. in Environmental Engineering from Tongji University in 2020. I am currently an AI Algorithm Engineer.

简历

Publications

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Learning-based modelling and real-time control of urban drainage networks

Zhiyu Zhang , Zhenliang Liao , Zhiguo Yuan
Doctoral thesis, 2026

Learning-based hydraulic modelling and real-time control of urban drainage networks.

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Efficient reinforcement learning for urban drainage control via a neural network-based model using truncated and parallel rollouts
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Efficient reinforcement learning for urban drainage control via a neural network-based model using truncated and parallel rollouts

Zhiyu Zhang , Wenchong Tian , Zhenliang Liao , Zhiguo Yuan
Water Research, 2026

Accelerates RL training with GPU parallelization while ensuring reliability with truncated rollouts.

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Differentiable neural network-based models enable gradient-based optimization for model predictive control of urban drainage networks
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Differentiable neural network-based models enable gradient-based optimization for model predictive control of urban drainage networks

Zhiyu Zhang , Wenchong Tian , Zhenliang Liao , Zhiguo Yuan
Water Research, 2026

Derives the gradients of the MPC objective function (output) to the control action (input) along the neural network for a L-BFGS-B optimizer.

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Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks
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Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks

Zhiyu Zhang , Wenchong Tian , Chenkaixiang Lu , Zhenliang Liao , Zhiguo Yuan
Water Research, 2024

Control-oriented real-time modelling of drainage flow routing with a spatio-temporal GNN model with physics-guided hydraulic regulations.

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Towards coordinated and robust real-time control: a decentralized approach for combined sewer overflow and urban flooding reduction based on multi-agent reinforcement learning
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Towards coordinated and robust real-time control: a decentralized approach for combined sewer overflow and urban flooding reduction based on multi-agent reinforcement learning

Zhiyu Zhang , Wenchong Tian , Zhenliang Liao
Water Research, 2023

Using multi-agent reinforcement learning to control multiple drainage valves and pumps with real-time communication fail-safe performance.

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Toward the full utilization of storage capacity: Urban drainage system operation integrating deep reinforcement learning and green infrastructure
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Toward the full utilization of storage capacity: Urban drainage system operation integrating deep reinforcement learning and green infrastructure

Xinran Wu , Wenchong Tian , Zhiyu Zhang , Alessandro Stocchino , Xinyu He , Zhenliang Liao , Zhiguo Yuan
Water Research, 2027

Integrates a green infrastructure cooperative index into deep reinforcement learning to improve storage utilization and reduce flooding and sewer overflows.

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A graph-based frequency-domain model enables highly efficient modelling of sewer network hydraulics
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A graph-based frequency-domain model enables highly efficient modelling of sewer network hydraulics

Shixun Li , Wenchong Tian , Zhiyu Zhang , Wei Zeng , Zhiguo Yuan
Water Research, 2026

Introduces a graph-based frequency-domain model for highly efficient sewer hydraulic simulation by decoupling spatial and temporal dependencies.

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Intelligent monitoring and management of urban drainage systems through machine-learning-driven fusion of EEM and UV-Vis
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Intelligent monitoring and management of urban drainage systems through machine-learning-driven fusion of EEM and UV-Vis

Hao-Lin Yang , Xin Zhang , Shang-Wen Deng , Jia-Min Xu , Ruo-Zhou Lin , Zhiyu Zhang , Yu-Qi Wang , Ding-Ding Tang , Hong-Cheng Wang
Journal of Environmental Management, 2026

Combines EEM fluorescence, UV-Vis spectroscopy, and machine learning for reagent-free dissolved organic carbon monitoring across urban drainage systems.

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Panning for Gold: Finding Representative Water Quality Dynamic Data for Data-Efficient Machine Learning through Koopman Analysis
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Panning for Gold: Finding Representative Water Quality Dynamic Data for Data-Efficient Machine Learning through Koopman Analysis

Wenchong Tian , Yu-Qi Wang , Zhiyu Zhang , Ruo-Zhou Lin , Hao-Lin Yang , Hao Wu , Hexiang Yan , Kunlun Xin , Tao Tao , Hong-Cheng Wang
Water & Ecology, 2026

Selects representative water quality dynamics using Koopman analysis and clustering to reduce data requirements for machine learning in wastewater treatment.

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Estimating methane emissions from global sewer networks
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Estimating methane emissions from global sewer networks

Keshab Sharma , Jiuling Li , Tao Liu , John Willis , Yiwen Liu , Zhiyu Zhang , Zhiguo Yuan
Nature Water, 2026

Develop a simple equation to estimate CH4 emissions and extrapolate to a global scale of 1.18 - 1.95 Mt/year.

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A knowledge-data fusion framework accelerates deep reinforcement learning for real-time control of urban drainage systems
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A knowledge-data fusion framework accelerates deep reinforcement learning for real-time control of urban drainage systems

Wenchong Tian , Zhiyu Zhang , Xuan Wang , Hexiang Yan , Zhenliang Liao , Kunlun Xin , Tao Tao , Zhiguo Yuan
Water Research, 2026

Pretrain RL agent for RTC with knowledge-carrying data from engineering experiences and strategies.

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Enhancing the resilience of urban drainage system using deep reinforcement learning
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Enhancing the resilience of urban drainage system using deep reinforcement learning

Wenchong Tian , Zhiyu Zhang , Kunlun Xin , Zhenliang Liao , Zhiguo Yuan
Water Research, 2025

Use the derivative of UDS resilience indicator to train a deep reinforcement learning agent.

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Transformer Networks and Loss with Punishment for Optimized Management of Urban Water Supply System
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Transformer Networks and Loss with Punishment for Optimized Management of Urban Water Supply System

Yu-Qi Wang , Hong-Cheng Wang , Jia-Ji Chen , Wan-Xin Yin , Jiuling Li , Zhiyu Zhang , He-Wen Li , Mei-Fang Wang , Ai-Jie Wang
ACS ES&T Water, 2025

Use transformer networks and loss with punishment to optimize management of urban water supply system.

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An integrated assessment of urban flooding risk and resilience based on spatial grids
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An integrated assessment of urban flooding risk and resilience based on spatial grids

Zhenliang Liao , Xinyu He , Wenchong Tian , Zhiyu Zhang , Huijuan Wang , Wanying Xie
Urban Water Journal, 2025

Assess both risk and resilience with 1D2D modelling and threshold classifications.

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The Use of Attention-Enhanced CNN-LSTM Models for Multi-Indicator and Time-Series Predictions of Surface Water Quality
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The Use of Attention-Enhanced CNN-LSTM Models for Multi-Indicator and Time-Series Predictions of Surface Water Quality

Minhao Zhang , Zhiyu Zhang , Xuan Wang , Zhenliang Liao , Lijin Wang
Water Resources Management, 2024

Use spatial and temporal attention mechanisms to enhance CNN-LSTM models for predicting surface water quality.

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Source identification of water distribution system contamination based on simulated annealing-particle swarm optimization algorithm
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Source identification of water distribution system contamination based on simulated annealing-particle swarm optimization algorithm

Zhenliang Liao , Xingyang Shi , Yangting Liao , Zhiyu Zhang
Environmental Monitoring and Assessment, 2024

Avoid local optima problem and ensure effcient locatilization of contaminated sources in WDS combining SA and PSO.

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A 'Prediction - Detection - Judgment' framework for sudden water contamination event detection with online monitoring
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A 'Prediction - Detection - Judgment' framework for sudden water contamination event detection with online monitoring

Zhenliang Liao , Minhao Zhang , Yun Chen , Zhiyu Zhang , Huijuan Wang
Journal of Enviornmental Management, 2024

Detect contamination timely and accurately from monitoring data considering water quality baseline and possible anomalies.

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Improving the interpretability of deep reinforcement learning in urban drainage system operation
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Improving the interpretability of deep reinforcement learning in urban drainage system operation

Wenchong Tian , Guangtao Fu , Kunlun Xin , Zhiyu Zhang , Zhenliang Liao
Water Research, 2024

Interpret the learned control policy of DRL with Sobol, tree-based logics, and conditional distributions.

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Enhancing interpretability and generalizability of deep learning-based emulator in three-dimensional lake hydrodynamics using Koopman operator and transfer learning: Demonstrated on the example of lake Zurich
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Enhancing interpretability and generalizability of deep learning-based emulator in three-dimensional lake hydrodynamics using Koopman operator and transfer learning: Demonstrated on the example of lake Zurich

Wenchong Tian , Zhiyu Zhang , Damien Bouffard , Hao Wu , Kunlun Xin , Xianyong Gu , Zhenliang Liao
Water Research, 2024

Learning a 3D hydrodynamic lake model from monitoring data using Koopman operator and transfer learning.

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Flooding mitigation through safe & trustworthy reinforcement learning
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Flooding mitigation through safe & trustworthy reinforcement learning

Wenchong Tian , Kunlun Xin , Zhiyu Zhang , Muhan Zhao , Zhenliang Liao , Tao Tao
Journal of Hydrology, 2023

Safe learning control of urban drainage systems with a robust reward function.

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Comparison of Real-time Control Methods for CSO Reduction with Two Evaluation Indices: Computing Load Rate and Double Baseline Normalized Distance
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Comparison of Real-time Control Methods for CSO Reduction with Two Evaluation Indices: Computing Load Rate and Double Baseline Normalized Distance

Zhenliang Liao , Zhiyu Zhang , Wenchong Tian , Xianyong Gu , Jiaqiang Xie
Water Resources Management, 2022

Compare rule-based control, model predictive control, and reinforcement learning control in computing load and normalized performance.

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Flooding and overflow mitigation using deep reinforcement learning based on Koopman operator of urban drainage systems
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Flooding and overflow mitigation using deep reinforcement learning based on Koopman operator of urban drainage systems

Wenchong Tian , Zhenliang Liao , Zhiyu Zhang , Hao Wu , Kunlun Xin
Water Resources Research, 2022

Training a Koopman emulator of urban drainage dynamis for reinforcement learning control.

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Combined Sewer Overflow and Flooding Mitigation Through a Reliable Real-Time Control Based on Multi-Reinforcement Learning and Model Predictive Control
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Combined Sewer Overflow and Flooding Mitigation Through a Reliable Real-Time Control Based on Multi-Reinforcement Learning and Model Predictive Control

Wenchong Tian , Zhenliang Liao , Guozheng Zhi , Zhiyu Zhang , Xuan Wang
Water Resources Research, 2022

RL agents' decisions are action votes and further evaluated in real time to improve reliability and safety.

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