Data-driven interval credibility constrained quadratic programming model for water quality management under uncertainty

Although integrated simulation-optimization modeling can provide a comprehensive and reliable analysis for water quality management (WQM), it is usually not easy to implement in practice. This study proposed a new efficient simulation-optimization modeling approach by leveraging the power of data-dr...

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Veröffentlicht in:Journal of environmental management Jg. 293; S. 112791
Hauptverfasser: Zhang, Qianqian, Li, Zhong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.09.2021
Schlagworte:
Canada
case studies
Data-driven modeling
DICCQP
environmental management
Grand River simulation model
Inexact linear regression
management systems
phosphorus
regression analysis
river water
rivers
simulation models
total phosphorus
Uncertainty
water quality
Water quality management
watersheds
Canada
Data-driven modeling
Grand River simulation model
DICCQP
Uncertainty
Water quality management
Inexact linear regression
ISSN:0301-4797, 1095-8630, 1095-8630
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Zusammenfassung:Although integrated simulation-optimization modeling can provide a comprehensive and reliable analysis for water quality management (WQM), it is usually not easy to implement in practice. This study proposed a new efficient simulation-optimization modeling approach by leveraging the power of data-driven modeling, to support WQM under various uncertainties. A water quality simulation model is integrated with the optimization model, and then substituted by a series of numerical surrogate models based on inexact linear regression. The transformation can significantly reduce the computational burden and make it possible to implement uncertainty quantification through hybrid inexact programming. The proposed model incorporates interval quadratic programming and credibility constrained programming to deal with nonlinearity and various uncertainties associated with the management system. The proposed approach is applied to a real case study of the Grand River watershed in Canada for controlling phosphorus concentration in river water. The Grand River Simulation Model (GRSM) is employed as the physical simulation model to estimate the total phosphorus concentration in the river. Interval solutions under different confidence levels of violating the effluent standards were obtained, which can be used to generate optimal phosphorus control strategies. The results indicate the proposed data-driven interval credibility constrained quadratic programming (DICCQP) model is able to provide reliable and robust solutions for WQM by considering nonlinearity and various uncertainties while maintaining a high computational efficiency. The proposed new framework can be extended and applied to the other watersheds. The high efficiency of the proposed model makes it possible to solve large-scale complex water quality management and planning problems. •A new data-driven modeling based simulation-optimization framework is proposed for WQM.•Various uncertainties are quantified by hybrid inexact programming.•The proposed model improves the optimization efficiency.•The model is applied in the Grand River to control and manage TP loadings.•The model can be extended to other watersheds to solve complex large-scale problems.
Bibliographie:ObjectType-Article-1
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ISSN:0301-4797
1095-8630
1095-8630
DOI:10.1016/j.jenvman.2021.112791