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研究报告

摘要：流域系统具有复合性和非线性特征,其机理过程的数值描述本身也存在随机和认知不确定性.在此前提下,如何优化调控流域系统,实现水质改善和水生态系统健康成为当前水资源、水环境及水生态领域亟待解决的突出问题.本文提出了贝叶斯递归回归树模型和区间不确定性的风险决策分析方法,与强化区间线性规划结合形成了新的不确定性"模拟-优化"间接耦合模型,实现流域系统优化调控及风险决策方案.上述方法将应用于美国佛杰尼亚州Chesterfield县Swift Creek水库营养盐TMDL分配方案,确定8个子流域的最小负荷分配以实现叶绿素a浓度达标.结果表明该耦合模型既能表征流域系统特征,也能兼顾不确定性模拟的准确度、计算效率以及不确定性优化与风险决策的解空间全局最优性、绝对可行性,为流域系统优化调控及其未来非线性系统的预测与最优控制提供了新的计算工具.

Abstract：Watershed systems are complex and nonlinear, and stochastic and cognitive uncertainties exist in the numerical description of their scheme. In this context, optimizing watershed systems for improving water quality and ecological health is essential for integrated watershed management. To support watershed system regulation and its risk-based decision-making, Bayesian Recursive Regression Tree model (BRRT) and risk-based decision-making analysis under interval-uncertainty were proposed, which coupled with Enhanced-Interval Linear Programming (EILP) formed a new indirect coupled "simulation-optimization" model. The proposed approach 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-a criteria. The results indicated that the proposed model is able to represent watershed system characteristics; more importantly, it improves the prediction accuracy, computational efficiency of process simulation as well as global optimality and absolute feasibility of optimization algorithm. This model could be applied for forecast and optimal controls of the analogous nonlinear systems.

Key words：Simulation-optimization decision tree optimization under uncertainty total maximum daily load (TMDL) watershed process algea dynamics