Abstract

Using a Sequential Convolutional Neural Network (CNN) model, this research investigates the use of neural networks for the categorization of oil spills in satellite data. The increasing occurrence of oil spills presents a substantial danger to marine ecosystems, hence requiring expeditious and precise methodologies for detection. The suggested technique utilises the hierarchical feature learning capabilities of Convolutional Neural Networks (CNNs) to autonomously identify pertinent patterns from satellite photos. This enables the differentiation between oil spills and natural water characteristics. The use of a sequential design significantly improves the network's capacity to grasp spatial relationships and underlying patterns present in the picture. The article presents an evaluation of the efficacy of the constructed Convolutional Neural Network (CNN) model by conducting thorough testing on various datasets. The results highlight the model's strong performance in accurately categorising oil spills, achieving a high accuracy rate of 96 percent. The use of neural networks in this particular context presents a potentially effective strategy for the prompt identification and monitoring of environmental risks, hence enabling timely intervention and mitigation measures.