Spatial Data Mining for Prediction of Unobserved Zinc Pollutants Using Various Kriging Methods
- سال انتشار: 1402
- محل انتشار: مجله تحقیقات و فناوری پیشرفته محیطی، دوره: 1، شماره: 3
- کد COI اختصاصی: JR_JAERT-1-3_006
- زبان مقاله: انگلیسی
- تعداد مشاهده: 89
نویسندگان
Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada-۵۲۰۰۰۷, India
Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada-۵۲۰۰۰۷, India
Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada-۵۲۰۰۰۷, India
Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada-۵۲۰۰۰۷, India
چکیده
Following years of contamination, rivers may experience significant levels of heavy metal pollution. Our research aims to pinpoint hazardous areas in these rivers. In our specific case, we focus on the floodplains of the Meuse River contaminated with zinc (Zn). Elevated zinc concentrations can lead to various health issues, including anemia, rashes, vomiting, and stomach cramping. However, due to limited sample data on zinc concentrations in the Meuse River, it becomes imperative to generate missing data in unidentified regions. This study employs universal Kriging in spatial data mining to investigate and predict unknown zinc pollutants. The semivariogram serves as a valuable tool for illustrating the variability pattern of zinc. To predict concentrations in unknown regions, the model captured is interpolated using the Kriging method. Employing regression with geographic weighting allows us to observe how stimulus-response relationships change spatially. Various semivariogram models, such as Matern, exponential, and linear, are utilized in our work. Additionally, we introduce Universal Kriging and geographically weighted regression. Experimental findings indicate that: (i) the Matern model, determined by calculating the minimum error sum of squares, is the most suitable theoretical semivariogram model; and (ii) the accuracy of predictions is visually demonstrated by projecting results onto a real map.کلیدواژه ها
spatial data mining, missing data, semivariogram, Universal Kriging, Geographically weighted, Regressionاطلاعات بیشتر در مورد COI
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