Automated Real-Time Hydrocarbon Dispersion Modeling and Remediation Strategy Optimization using Hybrid Convolutional Neural Networks and Spatiotemporal Bayesian Filtering (RHD-SBF)

**Abstract:** This paper introduces a novel system, Automated Real-Time Hydrocarbon Dispersion Modeling and Remediation Strategy Optimization using Hybrid Convolutional Neural Networks and Spatiotemporal Bayesian Filtering (RHD-SBF), designed to dramatically improve efficiency and effectiveness in oil spill response. Leveraging advances in deep learning and probabilistic modeling, RHD-SBF provides a dynamic, high-resolution model of hydrocarbon dispersion, coupled with an optimized remediation strategy allocator capable of adapting to evolving conditions in real-time. This technology presents a concrete and commercially viable solution for oil spill mitigation, demonstrating significant improvements in predictive accuracy and resource allocation compared to traditional methods.