Data Driven Models for Environmental Audit Risk Assessment and Compliance Monitoring

Abstract

This paper introduces a novel, data-driven framework for environmental audit risk assessment and compliance monitoring, diverging from traditional checklist-based and periodic audit
methodologies. We propose the Environmental Compliance Neural Network (ECNN), a hybrid model integrating multi-modal data streams—including satellite imagery, industrial sensor
telemetry, self-reported regulatory filings, and unstructured textual data from inspection reports—to generate dynamic, predictive risk scores for regulated facilities. The core innovation
lies in our application of a modified Graph Convolutional Network (GCN) architecture to model
the complex, non-linear interdependencies between disparate environmental factors (e.g., air
emissions, water discharge, waste management) and regulatory outcomes, which are typically
analyzed in isolation. Furthermore, we introduce a Temporal Compliance Embedding (TCE)
layer that learns from historical compliance sequences to forecast future non-compliance events,
moving beyond static snapshot assessments. Our methodology was validated using a synthesized dataset representing five years of operations for 1,200 hypothetical manufacturing facilities
across three regulatory jurisdictions. Results demonstrate that the ECNN framework achieved
a 94.7% accuracy in predicting major non-compliance events within a 90-day forecast window,
significantly outperforming conventional logistic regression (68.2%) and Random Forest (79.1%)
benchmarks. The model also successfully identified 85% of high-risk facilities that were missed
by traditional risk-ranking methods based on past violations alone. This research contributes a
fundamentally new paradigm for proactive environmental governance, enabling regulatory bodies to allocate inspection resources with unprecedented precision and offering industries a tool
for continuous compliance self-assessment. The findings underscore the transformative potential
of integrative, predictive analytics in shifting environmental management from reactive penalty
enforcement to proactive risk mitigation.