A Bayesian-based two-stage inexact optimization method for supporting stream water quality management in the Three Gorges Reservoir region

Hu, X. H.; Li, Y. P.; Guo, Huiqing; Zhuang, Xiaowen; Ding, Xunliang

doi:10.1007/s11356-016-6106-6

Cited by 7 publications

(5 citation statements)

References 45 publications

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“…Tang et al [185,186] Heihe River Basin Li and Guo [107], Li et al [109,111] and Zhang et al [231] Heshui River Basin Li et al [117], Liu et al [128,132] and Xu et al [212] Huai River Basin Gu et al [73] and Li et al [112] Hun River Xu et al [215,216] Kaidu-Kongque River Basin Huang et al [88,90], Li et al [125], Zeng et al [222,224,[226][227][228][229] and Zhou et al [242] Lake Tai Watershed Liu et al [134] and Xu and Huang [217] Miyun Reservoir Han et al [81] and Rong et al [175] Nansihu Lake Basin Xie et al [207,209] Tarim River Basin Huang et al [88][89][90] Three Gorges Reservoir Feng et al [58], Han et al [79], Han et al [87], Huang et al [91], Li et al [126], Xu et al [211], Yuan et al [220] and Zhang et al [236] Xiangxi River Basin Han et al [79], Hu et al [86,87], Huang et al …”

Section: China Ertan Reservoirmentioning

confidence: 99%

“…Marinoni et al [140] propose a framework for planning major investment decisions and apply this to the case of a water quality enhancement program in a river catchment in Brisbane, Australia. Compromise programming is first used to score the options [42,87,118,121,126,132,139,150,174,176,208,213,224] Storm water [17] Wastewater [1,33,65,80,86,98,106,113,129,139,141,169,187,197,206,219,238] Water allocation [172,230,233] Water trading [224] Water treatment [33,206] Wetlands [39,207,225] for pollution reduction at various sites and the optimal investment problem is then formulated as a multicriteria knapsack problem. In some cases, legislation needs to be considered alongside other management strategies.…”

Section: Water Qualitymentioning

confidence: 99%

See 1 more Smart Citation

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Archibald

Marshall

2018

Environ Model Assess

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Economic development, variation in weather patterns and natural disasters focus attention on the management of water resources. This paper reviews the literature on the development of mathematical programming models for water resource management under uncertainty between 2010 and 2017. A systematic search of the academic literature identified 448 journal articles on water resource management for examination. Bibliometric analysis is employed to investigate the methods that researchers are currently using to address this problem and to identify recent trends in research in the area. The research reveals that stochastic dynamic programming and multistage stochastic programming are the methods most commonly applied. Water resource allocation, climate change, water quality and agricultural irrigation are amongst the most frequently discussed topics in the literature. A more detailed examination of the literature on each of these topics is included. The findings suggest that there is a need for mathematical programming models of large-scale water systems that deal with uncertainty and multiobjectives in an effective and computationally efficient way.

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Section: China Ertan Reservoirmentioning

confidence: 99%

Section: Water Qualitymentioning

confidence: 99%

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Archibald

Marshall

2018

Environ Model Assess

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“…Therefore, we still need a solution that adapts to the current situation to solve the huge environmental pollution disposal pressure. 5,6 Sudden water pollution is common in environmental pollution. Therefore, the sudden water pollution is introduced.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Method for Water Quality Emergency Monitoring in Environmental Pollution Accident Disposal

Ren

Wang

2023

Big Data

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Along with the country's comprehensive strength, the people's wealth is also more and more substantial, in every aspect of life has been significantly improved, people also pay more attention to environmental protection. The harm of environmental pollution should not be underestimated. Once the environmental pollution accident occurs, it must be handled promptly, or a series of consequences are very serious. Therefore, it is necessary to study the Bayesian method of water quality emergency monitoring in the disposal of environmental pollution accidents. The purpose of this article was to solve the slow progress of water quality monitoring in the disposal of environmental pollution accidents. Through the Bayesian method of environmental pollution accident disposal in water quality emergency monitoring, the target node variables, intermediate node variables, and evidence node variables are used for analysis. Construct a Bayesian network topology. This article uses the fuzzy Bayesian network risk assessment model to provide a reference for the emergency monitoring of water quality disposal in regional environmental pollution accidents and conduct risk assessment to facilitate the next work arrangement. The results show that the Bayesian method of water quality emergency monitoring in environmental pollution accident disposal can provide effective feedback. Compared with traditional methods, the risk assessment of water quality monitoring for environmental pollution accidents is 40% faster and 10% more accurate in calculating the probability of accidents caused by basic factors. The application of the Bayesian water quality emergency monitoring method in environmental pollution accident disposal is conducive to improving the efficiency of environmental pollution disposal, in-depth analysis of the existing problems in the current environmental emergency management work, and suggestions for improving the emergency mechanism of sudden environmental pollution accident. The purpose was to provide reference for the government to properly handle sudden environmental pollution accidents.

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“…Tavakoli [24] proposed a nonlinear two-stage stochastic fuzzy programming to tackle uncertainties described as fuzzy boundary intervals and probability distributions in decision making of optimal water allocation and pollutant load policies. Hu et al [25] proposed a Bayesian-based two-stage inexact optimization method for supporting water pollution control under uncertainty; optimal production patterns of economic activities under different pollutant discharge allowance scenarios were generated. Generally, one major limitation of TSP is that it can only account for the expected second-stage cost without any consideration on the variability of the recourse values.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Infinite Interval-Stochastic Risk Management Model for River Water Pollution Control under Uncertainty

Liu

Huang

et al. 2017

Water

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Abstract:In this study, a semi-infinite interval-stochastic risk management (SIRM) model is developed for river water pollution control, where various policy scenarios are explored in response to economic penalties due to randomness and functional intervals. SIRM can also control the variability of the recourse cost as well as capture the notion of risk in stochastic programming. Then, the SIRM model is applied to water pollution control of the Xiangxihe watershed. Tradeoffs between risks and benefits are evaluated, indicating any change in the targeted benefit and risk level would yield varied expected benefits. Results disclose that the uncertainty of system components and risk preference of decision makers have significant effects on the watershed's production generation pattern and pollutant control schemes as well as system benefit. Decision makers with risk-aversive attitude would accept a lower system benefit (with lower production level and pollutant discharge); a policy based on risk-neutral attitude would lead to a higher system benefit (with higher production level and pollutant discharge). The findings can facilitate the decision makers in identifying desired product generation plans in association with financial risk minimization and pollution mitigation.

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A Bayesian-based two-stage inexact optimization method for supporting stream water quality management in the Three Gorges Reservoir region

Cited by 7 publications

References 45 publications

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Bayesian Method for Water Quality Emergency Monitoring in Environmental Pollution Accident Disposal

A Semi-Infinite Interval-Stochastic Risk Management Model for River Water Pollution Control under Uncertainty

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