G.H. Huang’s research while affiliated with University of Regina and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (689)


A risk-based stochastic energy-water-carbon nexus analytical model to support provincial multi-system synergistic management - A case study of Shanxi, China
  • Article

November 2024

·

5 Reads

The Science of The Total Environment

Y L Zheng

·

G H Huang

·

Y P Li

·

[...]

·

W C Tang

The synergistic management of energy-water‑carbon (EWC) nexus systems is crucial for achieving the sustainable development goals (SDGs). Therefore, a non-deterministic interval chance-constrained fractional optimization model for EWC nexus system (ICCF-EWC) management has been developed in this study. This model is capable of handling uncertain parameters represented as stochastic probability distributions and interval values, providing an effective approach to addressing dual-objective optimization problems. Meanwhile, this model is expected to investigate the effect of water scarcity/carbon abatement pressure on the overall system, and potential synergistic abatement effects. A case study of Shanxi Province shows that over the next 30 years, the cumulative installed capacity for clean and renewable energy will exceed 65 %. The dominance of coal-fired electricity will be considerably diminished, with wind power overtaking coal/gas-fired power by around 30 %. Moreover, water scarcity and carbon mitigation pressure would promote the development of electricity conversion mode to clean energy rather than the large-scale carbon capture and storage (CCS) technology upgradation of thermal power. The results can help support low-carbon transition of power systems at a province-level in China and financial incentives related policy making to advance water conservation and carbon emission mitigation. The developed model can also be adapted to other energy resource-dependent regional power systems.


Unveiling China's household CO2 emissions with disaggregated energy sectors: An affinity-propagation multi-regional input-output model

November 2024

·

4 Reads

·

1 Citation

Renewable Energy

Although much research has focused on CO2 emissions driven by household consumption, significant challenges remain in capturing complex regional variations and the indirect contributions of disaggregated energy sectors and different income groups. In this study, an affinity-propagation multi-regional input-output (AP-MRIO) model is developed through incorporating Dagum Gini coefficient (DGC) and affinity propagation (AP) clustering within a multi-regional input-output (MRIO) modeling framework. AP-MRIO not only traces CO2 emissions from China’s provincial household consumption, particularly within disaggregated energy sectors, but also reveals the interaction between sector emissions and income levels. Results obtained disclose that (i) within energy sectors, thermal power, electric power distribution, and petroleum are major emitters (accounting for 68.7 %, 17.5 %, and 6.6 %, respectively); in comparison, CO2 emissions from renewable energy sectors (hydropower, nuclear, wind, and solar) are lower (2.7 %); (ii) urban middle- and high-income households contribute significantly to CO2 emissions (57.1 %), and notable carbon inequalities exist both within and between regions for energy sectors; (iii) some provinces (e.g., Inner Mongolia, Liaoning, and Heilongjiang) should prioritize reducing per capita emissions from the non-renewable energy sectors, and other provinces (e.g., Ningxia, Guangdong, and Hunan) should further promote the development of renewable energy and focus on emissions embodied in the use of intermediate products.







Multivariate time series convolutional neural networks for long-term agricultural drought prediction under global warming
  • Article
  • Full-text available

March 2024

·

103 Reads

·

3 Citations

Agricultural Water Management

Agricultural drought (AD) is disastrous to crop production and plant growth. The prediction of AD with sufficient lead time is helpful for developing agricultural water strategy, particularly under the context of global warming. However, the previous studies mainly focused on short lead times (1-6 months) and only used 3 or less variables to predict AD through copula models. In this study, a novel multivariate time series convolutional neural network (T-CNN) is developed to predict AD with long lead times based on multiple meteorological variables. To demonstrate its feasibility and novelty, T-CNN is used in the Aral Sea Basin (ASB) where agricultural production is dominant. Three global climate models (GCMs) and three shared socioeconomic pathways (SSPs) from CMIP6 are considered during 2026-2100. Results indicate that (1) precipitation, temperature, potential evapotranspi-ration, relative humidity and northward wind are significantly correlated with AD, and are selected as the predictors of AD; (2) compared with the conventional CNN and convolutional long short-term memory (ConvLSTM), T-CNN's performance is better, taking only about 10% of the computation time of ConvLSTM; (3) T-CNN can effectively extract the spatiotemporal characteristics of meteorological predictors and reproduce AD, showing high correlation coefficients (R>0.9) for 92.5% of the grids across ASB; (4) the result of simple model averaging (SMA) is better than other GCMs, indicating that the spatial differences in AD would become more pronounced with increasing time and emission level; (5) compared with the historical period, under SSP585, the extreme drought would increase 0.20 months/year (2026-2050), 0.23 months/year (2051-2075) and 0.28 months/year (2076-2100). The results highlight the spatiotemporal variation of AD in 21st century with a high resolution (0.1 • ×0.1 •), which can provide scientific support for agricultural water management and long-term drought prevention in ASB.

Download


Bayesian analysis of variance for quantifying multi-factor effects on drought propagation

March 2024

·

178 Reads

·

4 Citations

Journal of Hydrology

Drought poses a significant threat to agricultural sector, and meteorological drought (MD) is the main driver and origin of agricultural drought (AD). The process is susceptible to multiple potential factors, whereas there is a challenge in quantifying the effects of factors. This study develops a novel method named as BANOVA by combining Bayesian Model Averaging (BMA) with analysis of variance (ANOVA), and is tested in Central Asia which is an agricultural dominant area. Standardized Precipitation Index and Standardized Soil Moisture Index are calculated to characterize MD and AD from 1982 to 2014. Precipitation and soil moisture are provided by Climatic Research Unit Gridded Time Series and Global Land Data Assimilation System. The maximum Pearson correlation coefficients (MPCC) and drought propagation time (DPT) are selected to depict the process of drought propagation. The results suggest that BMA can effectively integrate the results of each machine learning model and reduce the uncertainty of model structure. Several findings can be summarized: (1) there is a stable relationship between AD and MD that MPCC are significant (p < 0.01) for all grids, and the average DPT is 2.8 months; (2) eight factors are selected for drought propagation simulation, and temperature plays the dominant role in both MPCC and DPT with the contribution of 86.2 % and 35.0 %; (3) the interactive effects of factors on drought propagation characteristics are significant, such as temperature and precipitation with the contribution of 13.2 % on drought propagation time. This study highlights the importance of temperature in drought propagation. Under the context of global warming, the propagation time and relationship between MD and AD will become shorter and closer, respectively.


Citations (80)


... Global warming has gradually become a challenge that we have to face. Extreme weather events directly or indirectly impact the socio-ecological system (Zhao et al. 2024;Liu et al. 2021Liu et al. , 2022. Both global and regional extreme events Xiong Zhou zhou.xiong@outlook.com ...

Reference:

Quantile delta-mapped spatial disaggregation analysis for summertime compound extremes over China
Projections of compound wet-warm and dry-warm extreme events in summer over China
  • Citing Article
  • July 2024

Journal of Hydrology

... Agriculture inherently influences both the economy and environment, with ACE being particularly influenced by factors such as business strategy, production approaches, resource capacity and technological proficiency (Cui et al 2024). Notably, while increased energy inputs increase agricultural economic returns, this often occurs at the expense of the environment, leading to increases in ACE (Wang et al 2024b). Moreover, agriculture primarily functions under a smallholder economy, relying on traditional, scattered and fragmented farms, limited to only serving the farmers' needs while failing to introduce any large-scale industrial cultivation (Zhong et al 2023). ...

An ecological input-output CGE model for unveiling CO2 emission metabolism under China's dual carbon goals
  • Citing Article
  • July 2024

Applied Energy

... Integrating these two steps into the decision-making process can thus optimize agricultural irrigation water allocation, improving the effectiveness and sustainability of water resource management in agriculture while considering the field water cycle (Yan & Li 2018). In addition, the double-sided stochastic fractional programming (DSFP) model handles uncertainty in water-agriculture-energy nexus (WAEN) systems, with a specific emphasis on climate change consequences (Zhang et al. 2024). The strategy is utilized to build an optimal water allocation methodology for a WAEN system, with a focus on mitigating the effects of climate change. ...

Improving efficiency and sustainability of water-agriculture-energy nexus in a transboundary river basin under climate change: A double-sided stochastic factional optimization method
  • Citing Article
  • March 2024

Agricultural Water Management

... The corresponding cable force values from these tests were input into the FE model for simulation, yielding 81 sets of results. A variance analysis was performed on the test results to deter-mine the significance of each parameter [37,38]. Due to the overall symmetry of the structure, the variance analysis results are provided only for sections 1# to 8#. ...

Bayesian analysis of variance for quantifying multi-factor effects on drought propagation

Journal of Hydrology

... Raihan, A. et al. [33] utilized Dynamic Ordinary Least Squares (DOLS) and Canonical Correlation Regression (CCR) to analyze the dynamic implications of factors such as economic growth and energy consumption on CDE, revealing the causal relationships among these factors. Wang, Z. et al. [34] identified the principal influencing factors and their interactions using factor analysis (FA) and subsequently employed a Bayesian Neural Network (BNN) to capture the nonlinear relationship between the inputs and CDE. Chang, L. et al. [35] exploited the capability of Projection Pursuit Regression (PPR) in handling high-dimensional data and extracted the most critical information for predicting CDE from large-scale datasets. ...

A factorial-analysis-based Bayesian neural network method for quantifying China's CO2 emissions under dual-carbon target
  • Citing Article
  • February 2024

The Science of The Total Environment

... LSTMs have been widely used to predict spatial precipitation patterns (dry-wet) (Gibson et al., 2021) and drought indices related to precipitation, such as the standardized precipitation index (SPI) (Poornima and Pushpalatha, 2019;Dikshit and Pradhan, 2021) and the standardized precipitation evapotranspiration index (SPEI) Dikshit et al., 2021;, excelling at capturing longterm dependencies. Beyond SPI and SPEI (Adikari et al., 2021;Dhyani and Pandya, 2021;Hao et al., 2023), CNNs have been applied for predicting other indices, such as the soil moisture index (SMI) (Dhyani and Pandya, 2021) and soil moisture condition index (SMCI) (Zhang et al., 2024c), aiding agricultural drought prediction. Hybrid models like ConvLSTM and CNN-LSTM have demonstrated significant improvements in multi-temporal predictions for SPEI (Danandeh Mehr et al., 2023;Nyamane et al., 2024) and SPI (Park et al., 2020), as well as indices like the scaled drought condition index (SDCI) (Park et al., 2020), composite drought index (CDI) (Zhang et al., 2023a), and Palmer drought severity index (PDSI) (Elbeltagi et al., 2024). ...

Multivariate time series convolutional neural networks for long-term agricultural drought prediction under global warming

Agricultural Water Management

... Statistical downscaling methods are frequently used to downscale the prediction data from GCMs. These methods are widely recognized as one of the most useful approaches for handling climate data (Liu et al., 2023). Multi-model ensembles can effectively integrate the advantages of a single climate model to reduce the stochastic errors of the model. ...

Development of an integrated model on the basis of GCMs-RF-FA for predicting wind energy resources under climate change impact: A case study of Jing-Jin-Ji region in China
  • Citing Article
  • October 2023

Renewable Energy

... Most conventional treatment procedures have several variables. To maximize the treatment performance, optimization plays a crucial role [22][23][24]. Response surface methodology (RSM) is one of the optimization methods that is used to provide a great deal of data [21,[25][26][27]. It is a set of mathematical and statistical approaches for constructing models, analyzing the impacts of multiple variables, and determining the values of process variables that result in desirable response values [28][29][30]. ...

Sustainable management of water-economy-ecology nexus through coupling bi-level fractional optimization with effluent-trading mechanism: A case study of Dongjiang watershed
  • Citing Article
  • August 2023

Ecological Indicators

... Informed by the literature (Wang et al., 2024;Xu et al., 2023), 28 parameters related to streamflow were selected. After multiple calibration trials, 13 parameters with marked sensitivity were ultimately selected for model calibration. ...

Developing an integrated PCE-ANOVA-RF method for uncertainty quantification of hydrological model – The Amu Darya River Basin in Central Asia
  • Citing Article
  • July 2023

Journal of Hydrology

... There was still a lack of research on the combined driving effects of precipitation and potential evapotranspiration on regional ETa. The Copula function, as an effective method for constructing joint distributions of multidimensional variables, has been widely used in the field of hydrology [20,21]. The advantage of the Copula function is that it does not require variables to have the same marginal distribution. ...

Copula function with Variational Bayesian Monte Carlo for unveiling uncertainty impacts on meteorological and agricultural drought propagation
  • Citing Article
  • May 2023

Journal of Hydrology