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首页> 外文期刊>Journal of Geographic Information System >Detection and Mapping of Water Quality Variation in the Godavari River Using Water Quality Index, Clustering and GIS Techniques
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Detection and Mapping of Water Quality Variation in the Godavari River Using Water Quality Index, Clustering and GIS Techniques

机译:利用水质指数,聚类和GIS技术检测和绘制戈达瓦里河水质变化的地图

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The objective of this research is to develop a tool for planning and managing the water quality of River Godavari. This is achieved by classifying the pollution levels of Godavari River into several categories using water quality index and a clustering approach that ensure simple but accurate information about the pollution levels and water characteristics at any point in Godavari River in Maharashtra. The derived water quality indices and clusters were then visualized by using a Geographical Information System to draw thematic maps of Godavari River, thus making GIS as a decision support system. The obtained maps may assist the decision makers in managing and controlling pollution in the Godavari River. This also provides an effective overview of those spots in the Godavari River where intensified monitoring activities are required. Consequently, the obtained results make a major contribution to the assessment of the State’s water quality monitoring network. Three significant groups (less polluted, moderately and highly polluted sites) were detected by Cluster Analysis method. The results of Discriminant Analysis revealed that five parameters?i.e.?pH, Dissolved Oxygen (DO), Faecal Coliform (FC), Total Coliform (TC) and Ammonical Nitrogen (NH3-N) were necessary for analysis in spatial variation. Using discriminant function developed in the analysis, 100% of the original sites were correctly classified.
机译:这项研究的目的是开发一种用于规划和管理戈达瓦里河水质的工具。这可以通过使用水质指数和聚类方法将戈达瓦里河的污染水平分为几类来实现,以确保在马哈拉施特拉邦戈达瓦里河任何一点的污染水平和水质信息简单而准确。然后,通过使用地理信息系统绘制戈达瓦里河的专题地图,可视化得出的水质指标和集群,从而使GIS成为决策支持系统。获得的地图可以帮助决策者管理和控制戈达瓦里河中的污染。这也有效地概述了戈达瓦里河中需要加强监测活动的那些地点。因此,所获得的结果为该州水质监测网络的评估做出了重要贡献。通过聚类分析方法检测到三个重要的组(污染程度较低,中度和高度污染的地点)。判别分析的结果表明,空间变异分析需要五个参数,即pH,溶解氧(DO),粪便大肠菌群(FC),总大肠菌群(TC)和氨氮(NH3-N)。使用分析中开发的判别函数,可以正确分类100%的原始位点。

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