Shipeng Chu

449 total citations
36 papers, 314 citations indexed

About

Shipeng Chu is a scholar working on Civil and Structural Engineering, Ocean Engineering and Water Science and Technology. According to data from OpenAlex, Shipeng Chu has authored 36 papers receiving a total of 314 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Civil and Structural Engineering, 17 papers in Ocean Engineering and 11 papers in Water Science and Technology. Recurrent topics in Shipeng Chu's work include Water Systems and Optimization (30 papers), Water resources management and optimization (13 papers) and Water Treatment and Disinfection (8 papers). Shipeng Chu is often cited by papers focused on Water Systems and Optimization (30 papers), Water resources management and optimization (13 papers) and Water Treatment and Disinfection (8 papers). Shipeng Chu collaborates with scholars based in China, United States and Brazil. Shipeng Chu's co-authors include Yu Shao, Tuqiao Zhang, Tingchao Yu, Xiaowei Liu, Cong Li, Guilin He, Xingmao Ma, Feilong Dong, Qiufeng Lin and Yanxi Yu and has published in prestigious journals such as The Science of The Total Environment, Water Research and Journal of Cleaner Production.

In The Last Decade

Shipeng Chu

32 papers receiving 307 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Shipeng Chu China 12 206 107 92 64 63 36 314
in Chief Ghana 4 262 1.3× 97 0.9× 85 0.9× 105 1.6× 45 0.7× 13 370
Marco van Dijk South Africa 11 149 0.7× 131 1.2× 61 0.7× 25 0.4× 67 1.1× 39 382
Hongbin Zhao China 10 311 1.5× 118 1.1× 94 1.0× 78 1.2× 45 0.7× 25 364
Philip Jonkergouw United Kingdom 5 299 1.5× 88 0.8× 84 0.9× 115 1.8× 17 0.3× 6 321
I. Ethem Karadirek Türkiye 9 264 1.3× 108 1.0× 80 0.9× 69 1.1× 31 0.5× 19 316
Will Shepherd United Kingdom 9 194 0.9× 124 1.2× 36 0.4× 176 2.8× 15 0.2× 28 367
Tom Walski United States 10 462 2.2× 118 1.1× 121 1.3× 157 2.5× 54 0.9× 27 506
Xinyu Wan China 14 70 0.3× 211 2.0× 143 1.6× 135 2.1× 54 0.9× 38 498
Myounghak Oh South Korea 9 147 0.7× 76 0.7× 73 0.8× 42 0.7× 38 0.6× 46 401
Gopinathan R. Abhijith Israel 10 164 0.8× 76 0.7× 36 0.4× 113 1.8× 12 0.2× 33 260

Countries citing papers authored by Shipeng Chu

Since Specialization
Citations

This map shows the geographic impact of Shipeng Chu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shipeng Chu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shipeng Chu more than expected).

Fields of papers citing papers by Shipeng Chu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shipeng Chu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shipeng Chu. The network helps show where Shipeng Chu may publish in the future.

Co-authorship network of co-authors of Shipeng Chu

This figure shows the co-authorship network connecting the top 25 collaborators of Shipeng Chu. A scholar is included among the top collaborators of Shipeng Chu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shipeng Chu. Shipeng Chu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Zhang, Tuqiao, Xiangyun Zhang, Yu Shao, et al.. (2025). Multi-scale Spatio-temporal graph neural network for enhanced water demand forecasting. Water Research. 288(Pt B). 124711–124711.
3.
Yu, Tingchao, et al.. (2025). Pressure Sensor Placement for Pipe Burst Detection and Localization in Water Distribution System. Journal of Water Resources Planning and Management. 151(7).
4.
Ostfeld, Avi, et al.. (2025). Leveraging Spatiotemporal Redundancy for Sensor Data Imputation in Water Distribution Networks. Water Resources Research. 61(9). 1 indexed citations
5.
Chu, Shipeng, et al.. (2025). Sensor Cooperation Gain System for Burst Monitoring in Water Distribution Network: Concept, Design, and Evaluation. Water Resources Research. 61(3). 1 indexed citations
6.
Wang, Changjiang, Wei Qian, Shuanglin Shen, et al.. (2025). Enhanced semi-supervised model for acoustic leak detection in water distribution networks. Automation in Construction. 175. 106228–106228. 3 indexed citations
7.
Yu, Huimin, et al.. (2024). Leak detection in water distribution networks based on deep learning and kriging interpolation method. AQUA - Water Infrastructure Ecosystems and Society. 73(8). 1741–1753. 3 indexed citations
8.
Shao, Yu, et al.. (2024). Pressure Sampling Design for Estimating Nodal Water Demand in Water Distribution Systems. Water Resources Management. 38(4). 1511–1527. 5 indexed citations
9.
Zhou, Xinhong, Shipeng Chu, Tuqiao Zhang, Tingchao Yu, & Yu Shao. (2024). Risk‐Constrained Optimal Scheduling in Water Distribution Systems Toward Real‐Time Pricing Electricity Market. Water Resources Research. 60(4). 1 indexed citations
10.
Shao, Yu, et al.. (2023). Pump scheduling optimization in water distribution system based on mixed integer linear programming. European Journal of Operational Research. 313(3). 1140–1151. 9 indexed citations
11.
Shao, Yu, et al.. (2023). Noise Removal for the Steady-State Pressure Measurements Based on Domain Knowledge of Water Distribution Systems. Journal of Water Resources Planning and Management. 150(3). 1 indexed citations
12.
Shao, Yu, Kun Li, Tuqiao Zhang, Y. Jeffrey Yang, & Shipeng Chu. (2023). Modeling of truncated normal distribution for estimating hydraulic parameters in water distribution systems: taking nodal water demand as an example. Journal of Hydroinformatics. 25(5). 2053–2068. 4 indexed citations
13.
Shao, Yu, et al.. (2023). State estimation based on enhanced Bayesian approach: Application in water distribution systems. Control Engineering Practice. 134. 105461–105461. 1 indexed citations
14.
Shao, Yu, et al.. (2023). Global energy and leakage optimization in water distribution systems from water treatment plants to customer taps. Resources Conservation and Recycling. 194. 107003–107003. 4 indexed citations
15.
Shao, Yu, et al.. (2022). An improved hybrid community detection algorithm for partitioning of water distribution networks. Engineering Optimization. 56(3). 430–446. 4 indexed citations
16.
Li, Xin, Shipeng Chu, Tuqiao Zhang, Tingchao Yu, & Yu Shao. (2021). Leakage localization using pressure sensors and spatial clustering in water distribution systems. Water Science & Technology Water Supply. 22(1). 1020–1034. 17 indexed citations
17.
Chu, Shipeng, Tuqiao Zhang, Tingchao Yu, Quan J. Wang, & Yu Shao. (2021). A noise adaptive approach for nodal water demand estimation in water distribution systems. Water Research. 192. 116837–116837. 16 indexed citations
18.
Shao, Yu, Shipeng Chu, Tuqiao Zhang, Yanyan Yang, & Tingchao Yu. (2019). A Greedy Sampling Design Algorithm for the Modal Calibration of Nodal Demand in Water Distribution Systems. Mathematical Problems in Engineering. 2019(1). 1–3917571. 4 indexed citations
19.
Chu, Shipeng, et al.. (2019). Numerical approach for water distribution system model calibration through incorporation of multiple stochastic prior distributions. The Science of The Total Environment. 708. 134565–134565. 14 indexed citations
20.
Shao, Yu, Yanxi Yu, Tingchao Yu, Shipeng Chu, & Xiaowei Liu. (2019). Leakage Control and Energy Consumption Optimization in the Water Distribution Network Based on Joint Scheduling of Pumps and Valves. Energies. 12(15). 2969–2969. 22 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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