A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributes - Sulieman, Magboul M.; Kaya, Fuat; Al-Farraj, Abdullah S.; Brevik, Eric C. (MethodsX, 2025)
Keywords: air; chromium; cobalt; copper; edaphic factors; Environmental pollution-monitoring studies; Environmental Science; Environmental variables; Establish soil heavy metals background concentrations and spatial variability using machine learning algorithms coupled with remote sensing and digital elevation model derivatives; human health; humans; iron; Landsat; Landsat 8/9-OLI; landscapes; lead; manganese; nickel; pollution; prediction; radar; Random forest; regression analysis; soil; Stepwise multiple linear regression; topography; zinc; Environmental pollution-monitoring studies; Random forest; Landsat 8/9-OLI; Establish soil heavy metals background concentrations and spatial variability using machine learning algorithms coupled with remote sensing and digital elevation model derivatives; Environmental variables; Stepwise multiple linear regression