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Search for potential iron contamination zones in Burdwan district: an approach through fuzzy logic

机译:搜索Burdwan区的潜在铁污区:通过模糊逻辑的方法

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Burdwan district is one of the iron affected areas in West Bengal. In the present research work, various controlling factors, i.e., geology, soil, rainfall, land use land cover are considered to delineate the potential iron contamination zones (PICZ) within Burdwan district. Assigning weights and ranks to various input factors and their sub-classes, respectively, are carried out in the ArcGIS 10.1 platform on the basis of Fuzzy logic technique. The PICZ, derived from modeling, is broadly classified into two classes, i.e., 'suitable' and 'unsuitable' where, 33% (2313 km~2) of the total area falls under unsuitable PICZ, indicating that the groundwater in these regions are unfit for drinking purpose due to iron contamination. The model output is further validated with three different kind of approaches, i.e., superimposition, success and prediction rate curve and ground truth verification. A total of 4727 reported points of iron contamination are used for superimposition technique where, an overall accuracy of 87% is obtained for the model prediction. Success rate curve (SRC) and prediction rate curve (PRC) shows an accuracy of 89% and 83%, respectively. Collection and analysis of water samples from the unsuitable PICZ for ground truth verification shows an accuracy of about 80%. The GIS technique has provided an efficient environment for analyses of handling large spatial data. The outcome of the research work will be helpful in better planning and management of groundwater resources.
机译:Burdwan District是西孟加拉邦的铁影响地区之一。在本研究工作中,各种控制因素,即地质,土壤,降雨,土地利用陆地覆盖被认为是在Burdwan区内描绘潜在的铁污染区(PICZ)。在ArcGIS 10.1平台基于模糊逻辑技术,分别为各种输入因子和它们的子类分配权重和它们的子类别。来自建模的PICZ广泛分为两类,即“合适”和“不合适”,其中33%(2313 km〜2)的总面积在不合适的picz下落下,表明这些地区的地下水是由于铁污染而不适合饮酒。模型输出进一步验证了三种不同类型的方法,即叠加,成功和预测率曲线和地面真理验证。共有4727个报告的铁污染点用于叠加技术,在其中,为模型预测获得了87%的总精度。成功率曲线(SRC)和预测率曲线(PRC)分别显示了89%和83%的准确性。从不合适的PICZ中采集和分析地面真相验证的水样显示了约80%的准确性。 GIS技术提供了一种有效的环境,用于分析处理大型空间数据。研究工作的结果将有助于更好地规划和管理地下水资源。

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