Risk minimization in water quality control problems of a river system
Risk minimization in water quality control problems of a river system
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DOI：
10.1016/j.advwatres.2005.06.001
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发表时间：
2006-03
影响因子：
4.7
通讯作者：
Subimal Ghosh;P. Mujumdar
中科院分区：
文献类型：
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作者：
Subimal Ghosh;P. Mujumdar
提出了最小化河流水质管理问题风险的方法。开发风险最小化模型是为了在面对各利益相关者之间的冲突时最大限度地减少河流沿岸水质低的风险。该模型由三部分组成：水质模拟模型、带有不确定性分析的风险评估模型和优化模型。通过敏感性分析、一阶可靠性分析（FORA）和蒙特卡罗模拟来评估低水质的模糊风险。模糊多目标规划用于制定多目标模型。概率全局搜索洛桑（PGSL）是最近开发的一种全局搜索算法，用于解决由此产生的非线性优化问题。该算法基于这样的假设：在良好点集的邻域中更有可能找到更好的点集，因此加强了包含良好解的区域中的搜索。另一个模型是为了风险最小化而开发的，它只处理水质指标生成的概率密度函数的矩。确定了导致低模糊风险的水质指标的合适偏度值。将模型的结果与确定性模糊废物负荷分配模型 (FWLAM) 的结果进行比较，该模型采用稳态 BOD-DO 模型对印度南部通加-巴德拉河系统进行案例研究。风险最小化模型产生的去除分数略高，但会显着降低低水质风险。
Methodologies are presented for minimization of risk in a river water quality management problem. A risk minimization model is developed to minimize the risk of low water quality along a river in the face of conflict among various stake holders. The model consists of three parts: a water quality simulation model, a risk evaluation model with uncertainty analysis and an optimization model. Sensitivity analysis, First Order Reliability Analysis (FORA) and Monte–Carlo simulations are performed to evaluate the fuzzy risk of low water quality. Fuzzy multiobjective programming is used to formulate the multiobjective model. Probabilistic Global Search Laussane (PGSL), a global search algorithm developed recently, is used for solving the resulting non-linear optimization problem. The algorithm is based on the assumption that better sets of points are more likely to be found in the neighborhood of good sets of points, therefore intensifying the search in the regions that contain good solutions. Another model is developed for risk minimization, which deals with only the moments of the generated probability density functions of the water quality indicators. Suitable skewness values of water quality indicators, which lead to low fuzzy risk are identified. Results of the models are compared with the results of a deterministic fuzzy waste load allocation model (FWLAM), when methodologies are applied to the case study of Tunga–Bhadra river system in southern India, with a steady state BOD–DO model. The fractional removal levels resulting from the risk minimization model are slightly higher, but result in a significant reduction in risk of low water quality.