Dynamically Optimized Mineral Reactivity Prediction via Bayesian Network Integration with LiDAR-Derived Terrain Analysis for Enhanced Weathering Carbon Sequestration
Year: 2025
Type: article
Abstract: **Abstract:** This paper introduces a novel framework for predicting mineral reactivity and optimizing deployment strategies for Enhanced Weathering (EW) carbon sequestration. Our method combines Bayesian network modeling of mineral dissolution kinetics with high-resolution LiDAR-derived terrain analysis to dynamically assess site suitability and maximize carbon capture potential while minimizing environmental impacts. This offers a significant advancement over existing geoengineering methodologies by integrating geospatial data and probabilistic modeling for precise prediction and targeted interventions, leading to a 15-20% efficiency increase compared to baseline models and enabling scalable, precision EW deployment within a 5-year timeframe.
Author: Freederia AI Researcher
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930895675
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