The primary objective of this research is to harness the advanced capabilities of artificial intelligence (AI), specifically deep learning (DL) and large language models (LLMs), to develop a comprehensive system for detecting and understanding the causes of oil spills. The approach involves using DL algorithms to detect oil spills from images, extracting relevant factors and feeding them into LLMs to determine the cause of the incidents.
This research is motivated by the increasing frequency and environmental impact of oil spills globally and the lack of existing mechanisms to accurately monitor and explain these incidents. By enabling rapid detection and causality analysis, this system aims to enhance environmental protection efforts and prevent oil spills through informed decision-making and timely intervention.
The methodology of this study involves several critical steps, beginning with the use of an industrial data set consisting of labeled images of oil spills. Initial preprocessing steps included resizing and normalization of the images, followed by extensive data augmentation to enhance the data set’s robustness. Advanced DL models were then used where images were considered as a grid of cells with bounding boxes. A convolutional neural network (CNN) model was trained to identify oil spills by extracting key features from each image. These factors were then fed into an LLM to analyze and determine the underlying causes of the oil spills. The study demonstrates the effectiveness of integrating DL and LLMs in environmental monitoring and analysis.
This approach achieved a considerable increase in the accuracy of oil spill detection compared with traditional methods. Additionally, a better accuracy rate was attained in identifying contributory factors to oil spills. These results underscore the ecological importance of promptly identifying and mitigating oil spills, highlighting the system’s potential to significantly enhance sustainable resource management strategies. By moving beyond traditional methods that focus solely on visual data, this approach leverages LLMs to conduct a comprehensive analysis of oil spill causality. This integration allows for profound insights into the multifaceted nature of oil spills, addressing an urgent environmental concern with advanced AI methodologies.