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A Multi-Objective Artificial Bee Colony-based optimization approach to design water quality monitoring networks in river basins

机译:基于多目标人工蜂群的优化方法设计流域水质监测网络

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Water quality monitoring is important for the management of freshwater resources in river basins. Allocation of monitoring stations is the first step in the design of a water quality network. For this task, planning objectives are identified and a Multi-Objective Artificial Bee Colony-based optimization algorithm is designed and implemented in a Geographic Information System framework. Specifically, the number of stations is minimized in a range of values at the same time that the detection of lower compliance areas, the affected population and the relative importance of the river stretches are maximized. The estimation of pollutant parameters such as Biochemical Oxygen Demand, Faecal Coliform Bacteria or Total Dissolved Solids is performed by using the WorldQual model. This allows to objectively allocate monitoring stations to rivers where no real measurements are available, and thus it is especially relevant to allocate water quality stations for the first time. This approach has been tested on the Great Fish River basin (South Africa), finding networks improving the values of the objective functions between 22.22% and 47.83% with respect to the ones of the current network. Moreover, the solution analysis provides insightful and valuable information to the decision maker. (C) 2017 Elsevier Ltd. All rights reserved.
机译:水质监测对于流域淡水资源的管理非常重要。分配监测站是设计水质网络的第一步。为此,确定了计划目标,并在地理信息系统框架中设计并实现了基于多目标人工蜂群的优化算法。具体而言,在最大程度地降低对较低顺应性区域,受影响人口和河段的相对重要性的检测的同时,将站数在一定范围的值中最小化。使用WorldQual模型估算污染物参数,例如生化需氧量,粪便大肠菌或总溶解固体。这样可以客观地将监测站分配给没有实际测量值的河流,因此,首次分配水质站尤为重要。此方法已在大鱼河盆地(南非)上进行了测试,发现与现有网络相比,该网络将目标函数的值提高了22.22%至47.83%。此外,解决方案分析为决策者提供了有见地和有价值的信息。 (C)2017 Elsevier Ltd.保留所有权利。

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