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

**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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-22
DOI
https://doi.org/10.5281/zenodo.17172650
Primary Topic
Geochemistry and Geologic Mapping
Type
article
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article

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

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

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

article en

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.

Zenodo (CERN European Organization for Nuclear Research)
Freed–Hardeman University (US)
Life below water
Openalex Percentile: Top 31%
Geochemistry and Geologic Mapping
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