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

• Published in: MethodsX Vol. 14; p. 103180
• Main Authors: Sulieman, Magboul M., Kaya, Fuat, Al-Farraj, Abdullah S., Brevik, Eric C.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2025; Elsevier
• Subjects: 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

Soil heavy metals are among the most hazardous materials in the environment. Their harmful effects can extend to surrounding systems (air, plants, water), and given the appropriate conditions may ultimately have negative effects on human health. Thus, preventing pollution and protecting pristine soi...