Chengdu University of Information Technology, Chengdu, 610225, China; Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, L8S 4L7, ON, Canada.
Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, L8S 4L7, ON, Canada.
J Environ Manage. 2021 Sep 1;293:112791. doi: 10.1016/j.jenvman.2021.112791. Epub 2021 Jun 2.
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.
尽管综合模拟-优化建模可以为水质管理 (WQM) 提供全面可靠的分析，但在实践中通常不容易实施。本研究提出了一种新的高效模拟-优化建模方法，利用数据驱动建模的强大功能，支持各种不确定性下的 WQM。水质模拟模型与优化模型集成，然后通过基于不精确线性回归的一系列数值代理模型替代。这种转换可以显著降低计算负担，并通过混合不精确编程实现不确定性量化。所提出的模型结合了区间二次规划和可信度约束规划，以处理与管理系统相关的非线性和各种不确定性。该方法应用于加拿大格兰德河流域的一个实际案例研究，以控制河水中的磷浓度。格兰德河模拟模型 (GRSM) 被用作物理模拟模型来估计河水中的总磷浓度。获得了在违反排放标准的不同置信水平下的区间解，可用于生成最佳的磷控制策略。结果表明，所提出的数据驱动区间可信度约束二次规划 (DICCQP) 模型能够通过考虑非线性和各种不确定性，同时保持较高的计算效率，为 WQM 提供可靠和稳健的解决方案。所提出的新框架可以扩展并应用于其他流域。所提出模型的高效率使其能够解决大规模复杂的水质管理和规划问题。
