Oil Spill Classification using Machine Learning

An Oil Spill is the accidental discharge of liquid petroleum hydrocarbons into the environment. It is one of the major causes of marine and terrestrial pollution which affects marine ecosystems, marine animals most importantly, the economic system and human society. The study on this will help save lives, protect sensitive ecosystems, and minimize the economic impact of oil spills. This paper provides an overview of oil spill classification techniques using ML approaches, along with their advantages and limitations and recent advancements in the field. The classification techniques that are considered in this article are Random Forest Classifier, Decision Tree Classifier, K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Ensemble Learning Techniques such as Stacking. The outputs of each classification algorithm are analyzed and compared to determine the most effective method for classifying oil spills.

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