Exploring Spatial Data Mining Techniques: Predicting Zinc Concentration with Kriging Methods and Geographically Weighted Regression

Spatial data mining methods were used to predict zinc concentration in the Meuse River

Abstract

After years of contamination, rivers may get large amounts of heavy metal pollution. Our investigation's goal is to identify the river's hazardous locations. In our study case, we select the zinc-contaminated floodplains of the Meuse River (Zn). Excessive zinc levels may lead to various health issues, including anemia, rashes, vomiting, and cramping in the stomach. However, there isn't a lot of sample data available about the Meuse River's zinc concentration; as a result, it's necessary to generate the missing data in unidentified regions. This study employs universal Kriging in spatial data mining to explore and predict unknown zinc pollutants. The semivariogram is a useful tool for representing the variability pattern of zinc. This captured model will be interpolated using the Kriging method to predict the unknown regions. Regression with geographic weighting makes it possible to see how stimulus-response relationships change over space. We use a variety of semivariograms in our work, such as matern, exponential, and linear models. We also propose Universal Kriging and geographically weighted regression. The experimental findings show that: (i) the matern model, as determined by calculating the minimum error sum of squares, is the best theoretical semivariogram model; and (ii) the universal kriging predictions can be visually demonstrated by projecting the results onto the real map.