An Indirect Simulation-Optimization Model for Determining Optimal TMDL   Allocation under Uncertainty

doi:10.3390/w7116634

CORC > 北京大学 > 城市与环境学院

	An Indirect Simulation-Optimization Model for Determining Optimal TMDL Allocation under Uncertainty
	Zhou, Feng ; Dong, Yanjun ; Wu, Jing ; Zheng, Jiangli ; Zhao, Yue
刊名	WATER
	2015
关键词	total maximum daily load (TMDL) allocation eutrophication tradeoff analysis water-quality modeling uncertainty STEPWISE-CLUSTER-ANALYSIS SUPPORTING REMEDIATION QUALITY MANAGEMENT SYSTEM INFERENCE SCHEME
DOI	10.3390/w7116634
英文摘要	An indirect simulation-optimization model framework with enhanced computational efficiency and risk-based decision-making capability was developed to determine optimal total maximum daily load (TMDL) allocation under uncertainty. To convert the traditional direct simulation-optimization model into our indirect equivalent model framework, we proposed a two-step strategy: (1) application of interval regression equations derived by a Bayesian recursive regression tree (BRRT v2) algorithm, which approximates the original hydrodynamic and water-quality simulation models and accurately quantifies the inherent nonlinear relationship between nutrient load reductions and the credible interval of algal biomass with a given confidence interval; and (2) incorporation of the calibrated interval regression equations into an uncertain optimization framework, which is further converted to our indirect equivalent framework by the enhanced-interval linear programming (EILP) method and provides approximate-optimal solutions at various risk levels. The proposed strategy was applied to the Swift Creek Reservoir's nutrient TMDL allocation (Chesterfield County, VA) to identify the minimum nutrient load allocations required from eight sub-watersheds to ensure compliance with user-specified chlorophyll criteria. Our results indicated that the BRRT-EILP model could identify critical sub-watersheds faster than the traditional one and requires lower reduction of nutrient loadings compared to traditional stochastic simulation and trial-and-error (TAE) approaches. This suggests that our proposed framework performs better in optimal TMDL development compared to the traditional simulation-optimization models and provides extreme and non-extreme tradeoff analysis under uncertainty for risk-based decision making.; National Water Science and Technology Research Project [2013ZX07102-006]; National Natural Science Foundation of China [41201077]; Research Fund for the Doctoral Program of Higher Education of China [20120001120129]; 111 Project [B14001]; SCI(E); EI; ARTICLE; zhouf@pku.edu.cn; dyj@emails.bjut.edu.cn; jingwupku3@gmail.com; zhengjiangli2015@gmail.com; zhaoyue@caep.org.cn; 11; 6634-6650; 7
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436071]
专题	城市与环境学院
推荐引用方式 GB/T 7714	Zhou, Feng,Dong, Yanjun,Wu, Jing,et al. An Indirect Simulation-Optimization Model for Determining Optimal TMDL Allocation under Uncertainty[J]. WATER,2015.
APA	Zhou, Feng,Dong, Yanjun,Wu, Jing,Zheng, Jiangli,&Zhao, Yue.(2015).An Indirect Simulation-Optimization Model for Determining Optimal TMDL Allocation under Uncertainty.WATER.
MLA	Zhou, Feng,et al."An Indirect Simulation-Optimization Model for Determining Optimal TMDL Allocation under Uncertainty".WATER (2015).