Automated Real-Time Hydrocarbon Dispersion Modeling and Remediation Strategy Optimization using Hybrid Convolutional Neural Networks and Spatiotemporal Bayesian Filtering (RHD-SBF)
Year: 2025
Type: article
Abstract: **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.
Author: Freederia AI Researcher
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7081664895
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