Deep Learning Methodologies for Oil Spill Prevention: A Lifecycle-Based Sustainability Review

Oil Spills continue to pose significant risks across offshore production, marine transport, and subsea infrastructure. Deep Learning (DL) methods now support proactive Oil Spill Prevention (OSP) through Predictive Maintenance, Anomaly Detection, and Visual Surveillance. These approaches often achieve high performance but rely on computationally intensive workflows that demand substantial energy and generate carbon emissions. Such impacts remain largely unquantified in existing OSP research. This paper is a systematic review of DL-based OSP research published between 2020 and mid-2025, reported in accordance with the PRISMA 2020 guidelines. Ninety studies were analyzed to assess AI lifecycle practices across model architectures, data modalities, training regimes, evaluation methods, deployment choices, and sustainability considerations. Results show persistent reliance on accuracy-centric evaluation, with limited reporting of lifecycle energy use, carbon footprint, or hardware-related environmental impacts. To address this gap, a lifecycle-oriented sustainability framework is introduced. The framework operationalizes four metrics: Job Sustainability Cost , Amortized Sustainability Cost , Embodied Product Cost , and Expanded Amortized Sustainability Cost . These metrics capture operational energy demand, carbon intensity, embodied hardware emissions, and reuse effects across the AI lifecycle. Thus, supporting model comparison, compression strategies, carbon-aware scheduling, and lightweight performance logging. They integrate into routine OSP workflows by offline profiling of energy usage and carbon emissions during model development, evaluation, and scheduled audits. Profiling is performed outside live control loops, ensuring that safety-critical operational logic remains unaffected. By combining DL methodologies with lifecycle-based assessment, this study demonstrates how accuracy-centric evaluation can be complemented by transparent, resource-aware analysis. The proposed framework applies to OSP and is transferable to other safety-critical industrial monitoring domains.