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Qingzhou Zhang
Researcher at Zhejiang University
Publications - 34
Citations - 536
Qingzhou Zhang is an academic researcher from Zhejiang University. The author has contributed to research in topics: Population & Water supply network. The author has an hindex of 10, co-authored 32 publications receiving 244 citations. Previous affiliations of Qingzhou Zhang include Harbin Institute of Technology & Yanshan University.
Papers
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Journal ArticleDOI
Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines
TL;DR: In this paper, a multiclass support vector machine (M-SVM) is used as the leakage zone identification model and applied to determine the likely leakage zones with the observed field data.
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An efficient multi-objective optimization method for water quality sensor placement within water distribution systems considering contamination probability variations.
TL;DR: This paper provides an alternative method to identify optimal WQSP solutions for the WDS, and also builds knowledge regarding the impacts of different CPFs on sensor deployments.
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Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model
TL;DR: In this article, the performance of the LSTM-based model is compared with the autoregressive integrated moving average (ARIMA) model, support vector regression (SVR), and the random forests (RF) model based on data with time resolutions ranging from 15 min to 24 hours.
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Efficient Leak Localization in Water Distribution Systems Using Multistage Optimal Valve Operations and Smart Demand Metering
TL;DR: In this paper, a multistage method for burst leak localization through valve operations (VOs) and smart demand metering in district meter areas (DMAs) of water distribution systems (WDSs).
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Assessing the global resilience of water quality sensor placement strategies within water distribution systems
TL;DR: An evolutionary algorithm (EA) based method to systematically and efficiently investigate the WQSPS' global resilience considering all likely sensor failures is proposed and improved values of global resilience metrics are identified.