Oil-Contaminated Soil Modeling and Remediation Monitoring in Arid Areas Using Remote Sensing

Oil contamination is a major source of pollution in the environment. It may take decades for oil-contaminated soils to be remedied. This study models using one world’s greatest environmental disasters, onshore oil spill desert Kuwait 1991. work uses state-of-art remote sensing technologies and machine learning investigate spills during first Gulf War. We were able identify clear locations unsupervised classification over pre- post-oil data. The research area’s pre-war post-war circumstances, terms spills, discovered by developing spectral signatures with different wavelengths several indices utilized oil-contamination detection. Following that, we use this data sampling training model various soil levels. In addition, analyze two separate datasets used three modeling methodologies, Random Tree (RT), Support Vector Machine (SVM) Forest (RF). results show that suggested approach effective detecting soil. As result, location degree established. analysis can valid support studies an appropriate remediation.