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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Adsorptive removal of naphthalene induced by structurally different Gemini surfactants in a soil-water system.
TL;DR: This study focused on the use of symmetric and dissymmetric quaternary ammonium Gemini surfactants to immobilize naphthalene onto soil particles, and is used as an example of an innovative application to remove HOC in situ using the surfactant-enhanced sorption zone.
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Application of a GIS-based modeling system for effective management of petroleum-contaminated sites
TL;DR: In this article, a GIS-aided simulation (GISSIM) system is developed for effective management of petroleum-contaminated sites in a case study, where concentrations of benzene, toluene, and xylenes in groundwater under a petroleum contaminated site are dynamically simulated.
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Industry-environment system management based on an uncertain Gaussian diffusion optimization model for coal-dependent cities in ecologically fragile areas
TL;DR: IEM-UGOM is proved to be an efficient way on this study for reflecting the decision maker's attitude toward various adjustment scenarios of industrial production and environmental protection, and could help seek cost-effective management strategies under various credibility satisfaction levels and wind velocities.
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Two-Stage Inexact-Probabilistic Programming Model for Water Quality Management
Yongping Li,Wei Li,Guohe Huang +2 more
TL;DR: In this article, a two-stage inexact-probabilistic programming (TIPP) method was developed for water quality management in Zhangweinan River Basin, through coupling two-staged stochastic programming with inexact chance-constrained programming.
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Prediction of dust fall concentrations in urban atmospheric environment through support vector regression
TL;DR: This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels, and shows that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes.