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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Analysis of interactive effects of DEM resolution and basin subdivision level on runoff simulation in Kaidu River Basin, China
TL;DR: In this article, the effects of digital elevation model (DEM) resolution and basin subdivision level on runoff simulation with a semi-distributed land use-based runoff process model were evaluated for the Kaidu River Basin.
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Inexact two-phase fuzzy programming and its application to municipal solid waste management
TL;DR: An inexact two-phase fuzzy programming approach was proposed for municipal solid waste management and showed sound capability in identifying key factors and/or input conditions that may significantly affect system outputs, and thus facilitating the decision maker adjusting current system status to benefit the future management.
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Analyzing the performance of clean development mechanism for electric power systems under uncertain environment
TL;DR: Results indicate that CDM can create an opportunity for large-scale renewable energy project because Bazhou has abundant hydro and wind resources; compared with the basic case, renewable energy CDM project has advantages in realizing CO2-emission reduction as well as adjusting the local energy mix.
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Modelling of Atrazine Loss in Surface Runoff from Agricultural Watershed
TL;DR: In this article, an integrated modeling system was developed to estimate atrazine losses through surface runoff, which includes a distributed hydrological model, a pesticide adsorption model, relational databases, and a geographic information system.
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Electric power systems planning in association with air pollution control and uncertainty analysis
TL;DR: In this paper, a multistage stochastic full-infinite integer programming (MSFIP) method is developed for planning electric-power systems associated with multiple uncertainties presented in terms of crisp intervals, probability distributions, functional intervals and integer variables.