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首页> 外文期刊>Journal of Hydroinformatics >Fuzzy waste load allocation model: a multiobjective approach
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Fuzzy waste load allocation model: a multiobjective approach

机译：模糊废物负荷分配模型：多目标方法

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Fuzzy Waste Load Allocation Model (FWLAM), developed in an earlier study, derives the optimal fractional levels, for the base flow conditions, considering the goals of the Pollution Control Agency (PCA) and dischargers. The Modified Fuzzy Waste Load Allocation Model (MFWLAM) developed subsequently is a stochastic model and considers the moments (mean, variance and skewness) of water quality indicators, incorporating uncertainty due to randomness of input variables along with uncertainty due to imprecision. The risk of low water quality is reduced significantly by using this modified model, but inclusion of new constraints leads to a low value of acceptability level, λ, interpreted as the maximized minimum satisfaction in the system. To improve this value, a new model, which is a combination of FWLAM and MFWLAM, is presented, allowing for some violations in the constraints of MFWLAM. This combined model is a multiobjective optimization model having the objectives, maximization of acceptability level and minimization of violation of constraints. Fuzzy multiobjective programming, goal programming and fuzzy goal programming are used to find the solutions. For the optimization model, Probabilistic Global Search Lausanne (PGSL) is used as a nonlinear optimization tool. The methodology is applied to a case study of the Tunga-Bhadra river system in south India. The model results in a compromised solution of a higher value of acceptability level as compared to MFWLAM, with a satisfactory value of risk. Thus the goal of risk minimization is achieved with a comparatively better value of acceptability level.
机译：在较早的研究中开发的模糊废物负荷分配模型（FWLAM），考虑了污染控制机构（PCA）和排放者的目标，得出了基本流量条件下的最佳分数水平。随后开发的改进的模糊废物负荷分配模型（MFWLAM）是一个随机模型，它考虑了水质指标的矩（均值，方差和偏度），其中包括输入变量的随机性带来的不确定性以及不精确性带来的不确定性。通过使用此修改后的模型，可以大大降低水质低下的风险，但是包含新的约束条件会导致可接受水平λ的值较低，这被解释为系统中的最大最低满意度。为了提高此值，提出了一种新模型，该模型是FWLAM和MFWLAM的组合，允许在MFWLAM约束方面有一些违反。该组合模型是一个多目标优化模型，具有目标，可接受水平的最大化和约束约束的最小化。使用模糊多目标规划，目标规划和模糊目标规划来寻找解决方案。对于优化模型，洛桑概率全局搜索（PGSL）被用作非线性优化工具。该方法适用于印度南部通加-巴德拉河系统的案例研究。与MFWLAM相比，该模型得出的折衷解决方案的可接受性值更高，风险值令人满意。因此，以相对较好的可接受水平值来实现风险最小化的目标。

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