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Guangqian Wang

Researcher at Tsinghua University

Publications -  190
Citations -  4999

Guangqian Wang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Sediment & Precipitation. The author has an hindex of 36, co-authored 169 publications receiving 3592 citations. Previous affiliations of Guangqian Wang include Qinghai University & Zhejiang University.

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Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation

TL;DR: Two popular variants of Recurrent Neural Network named Long Short-Term Memory and Gated Recurrent Unit networks were employed to develop new data-driven flood forecasting models, showing that GRU models perform equally well as LSTM models and GRU may be the preferred method in short term runoff predictions.
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Phosphorous recovery through struvite crystallization: Challenges for future design.

TL;DR: This paper addresses the issue of phosphorous losses across the world by reviewing key aspects relevant to process design to pave the way for future application and provides comprehensive information that is essential for future industrial struvite crystallization process design.
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Modelling dam-break flows over mobile beds using a 2D coupled approach

TL;DR: In this article, a 2D morphodynamic model for predicting dam-break flows over mobile beds was developed, where the effects of sediment concentrations and bed evolution on the flood wave propagation were considered.
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Hydro Unit Commitment via Mixed Integer Linear Programming: A Case Study of the Three Gorges Project, China

TL;DR: In this article, the authors developed a methodology for optimizing the hydro unit commitment (HUC) for the Three Gorges Project (TGP) in China, where the objective is to minimize the total operational cost.
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Three-Dimensional Mathematical Model of Suspended-Sediment Transport

TL;DR: In this article, the basic equations for a mathematical model of sediment-laden flow in a nonorthogonal curvilinear coordinate system were derived using a tensor analysis of two-phase flow and incorporate a natural variable-density turbulence model with nonequilibrium sediment transport.