Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/2736
Title: Oil-Contaminated Soil Modeling and Remediation Monitoring in Arid Areas Using Remote Sensing
Authors: Kaplan, Gordana
Aydınlı, Hakan Oktay
Pietrelli, Andrea
Mieyeville, Fabien
Ferrara, Vincenzo
Keywords: oil contamination
soil pollution
soil contamination
soil remediation
remote sensing
arid areas
Random Forest
Gulf-War
Field
Kuwait
Impact
Issue Date: 2022
Publisher: Mdpi
Abstract: 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.
URI: https://doi.org/10.3390/rs14102500
https://hdl.handle.net/20.500.13087/2736
ISSN: 2072-4292
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

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