G
Guohe Huang
Researcher at Applied Science Private University
Publications - 1071
Citations - 30520
Guohe Huang is an academic researcher from Applied Science Private University. The author has contributed to research in topics: Stochastic programming & Fuzzy logic. The author has an hindex of 72, co-authored 979 publications receiving 25589 citations. Previous affiliations of Guohe Huang include Peking University & Beijing Normal University.
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A bi-level chance-constrained programming method for quantifying the effectiveness of water-trading to water-food-ecology nexus in Amu Darya River basin of Central Asia.
TL;DR: A bi-level chance-constrained programming (BCCP) method is developed for planning water-food-ecology (WFE) nexus system of the Amu Darya River basin, where the efficiency of water-trading mechanism and the impact of uncertain water-availability are examined.
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A risk-based interactive multi-stage stochastic programming approach for water resources planning under dual uncertainties
TL;DR: Results of comparison experiment indicate that RIMSP is able to provide more robust water management alternatives with less system risks in comparison with IMSP.
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Measurement of air-pollution inequality through a three-perspective accounting model.
TL;DR: A three-perspective atmospheric pollutant equivalents accounting model (or APE accounting model) for air-pollution inequality assessment under environmentally-extend multi-regional input-output framework is established and indicates that local emitters are merely parts of contributors to air pollution.
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A fractional factorial probabilistic collocation method for uncertainty propagation of hydrologic model parameters in a reduced dimensional space
TL;DR: In this article, a fractional factorial probabilistic collocation method is proposed to reveal statistical significance of hydrologic model parameters and their multi-level interactions affecting model outputs, facilitating uncertainty propagation in a reduced dimensional space.
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Planning regional-scale electric power systems under uncertainty: A case study of Jing-Jin-Ji region, China
TL;DR: Compared to the conventional stochastic programming, the developed CSFP method can more effectively analyze individual and interactive effects of multiple random variables, so that the loss of uncertain information can be mitigated and the robustness of solution can be enhanced.