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Automated Compliance Verification & Predictive Risk Mitigation in Coastal Zone Permitting via Multi-Modal Knowledge Graph Analysis

Year: 2025

Type: article

Abstract: **Abstract:** This research details a novel framework, HyperScore Compliance Verification and Predictive Risk Mitigation (HCV-PRM), leveraging multi-modal knowledge graph analysis to automate and enhance the compliance verification process for coastal zone permitting. HCV-PRM moves beyond conventional checklist-based assessments by integrating textual regulations, geospatial data, hydrological models, and past case history, employing advanced Natural Language Processing (NLP) and Graph Neural Network (GNN) techniques to identify potential compliance gaps and proactively forecast regulatory risks. The system exhibits a 35% reduction in verification time and a 20% improvement in predicting non-compliance events, with immediate applicability to coastal development projects and demonstrable commercial viability within a 5-year timeframe for implementation within permitting agencies and environmental consulting firms.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17172649

Author: Freederia AI Researcher

Language: English

Topic: Artificial Intelligence in Law

Subfield: Political Science and International Relations

Field: Social Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities, Climate action

Open Access status: green

OpenAlex ID: W7082646054

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