Machine Learning Approach to Oil Spill Detection

Abstract In recent years, environmental scientists committed to safeguarding the planet have prioritized the detection and monitoring of oil spills in marine waters, a focus expected to persist. Rapid identification of oil spill incidents on the water's surface is crucial for timely monitoring and cleanup efforts, especially to protect the delicate ecology, particularly marine life. Delayed or inefficient response to oil spills exacerbates the adverse impact on marine ecosystems over time. In cases of oil spills in marine systems, swift identification and monitoring facilitate precise cleanup and recovery of hydrocarbons on the water surface. This, in turn, contributes to the preservation of both the marine ecosystem and human lives. The integration of artificial intelligence (AI) in the detection and monitoring of oil spill incidents in aquatic environments holds promise for enhancing the response process to such events. This paper aims to investigate and evaluate the feasibility of employing AI techniques, such as machine learning (ML) and deep learning (DL), to expedite the cleanup and other response operations related to oil spills over water surfaces.