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

Automated summary

This study utilized remote sensing technologies and machine learning methods to investigate oil-contaminated soils in the context of the Kuwait desert oil spill in 1991. By analyzing pre- and post-spill data and developing spectral signatures, the researchers were able to detect and model the location and degree of contamination. The results demonstrate the effectiveness of the approach in detecting oil-contaminated soil.