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Home > Archives > Vol. 10 No. 11 (2025): published > Research Articles
ESP-4109

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2025-11-18

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Vol. 10 No. 11 (2025): published

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Research Articles

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Copyright (c) 2025 Adel Subhe Abedalkader Abraheem, Jameela Khedher Abbas, Duha Khalil Ibrahim Ahmed, Nazar Habeeb Abbas, Baker Mohammed Khalil, Petro Ponochovnyi

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Adel Subhe Abedalkader Abraheem, Jameela Khedher Abbas, Duha Khalil Ibrahim Ahmed, Nazar Habeeb Abbas, Baker Mohammed Khalil, & Petro Ponochovnyi. (2025). AI and machine learning in environmental monitoring: enhancing legal compliance and public trust. Environment and Social Psychology, 10(11), ESP-4109. https://doi.org/10.59429/esp.v10i11.4109
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AI and machine learning in environmental monitoring: enhancing legal compliance and public trust

Adel Subhe Abedalkader Abraheem

Al-Turath University, Baghdad 10013, Iraq

Jameela Khedher Abbas

Al-Mansour University College, Baghdad 10067, Iraq

Duha Khalil Ibrahim Ahmed

Al-Mamoon University College, Baghdad 10012, Iraq

Nazar Habeeb Abbas

Al-Rafidain University College, Baghdad 10064, Iraq

Baker Mohammed Khalil

Madenat Alelem University College, Baghdad 10006, Iraq

Petro Ponochovnyi

State University of Information and Communication Technologies, Kyiv, 03110, Ukraine


DOI: https://doi.org/10.59429/esp.v10i11.4109


Keywords: AI-driven monitoring; machine learning; environmental pollution; IoT sensors; predictive modeling; sustainability; legal compliance; public trust


Abstract

As environmental pollution becomes more complex over the years, finding effective monitoring methods becomes crucial. In real-time monitoring, artificial intelligence (AI) and machine learning (ML) models can be integrated to obtain information about air, water, and soil quality assessment. To improve the accuracy of pollution detection and forecasting, this study proposes a comprehensive framework that integrates IoT-enabled sensor networks, predictive AI models, and statistical validation techniques. The article assesses the relative performance of Gradient Boosting Machines (GBM), Long Short-Term Memory (LSTM) networks, and Transformer-based split networks to predict environmental changes.

The study was conducted across multi-domain urban, suburban, and rural monitoring zones using multimodal datasets derived from IoT sensors, remote sensing streams, and laboratory-validated environmental indicators. Similar integrated AI–IoT ecological monitoring strategies have been highlighted in recent literature as essential for sustainable environmental protection and high-fidelity pollution forecasting. The dataset comprised 216 air samples, 144 water samples, and 96 soil assays collected from three monitoring regions.

 Results show that PM2.5 concentrations decreased by 12% (p < 0.01), water turbidity declined by 15% (p < 0.01), and lead levels in soil were reduced by up to 16.1% in agricultural sites. The GBM model achieved the highest predictive performance with Root Mean Square Error (RMSE) = 2.1 µg/m³, Coefficient of Determination (R²) = 0.94, and F1-Score = 92.0%, outperforming LSTM and Transformer models.

Beyond technical performance, this study also highlights the legal and societal dimensions of AI-driven monitoring. By improving accuracy and transparency, these systems strengthen regulatory compliance frameworks while fostering public trust in environmental governance. Understanding how citizens and policymakers perceive the reliability of AI-based platforms is essential to ensuring policy acceptance and compliance behavior. This dual perspective—technological and psychological—illustrates that sustainable outcomes depend not only on advanced algorithms but also on social legitimacy and institutional accountability.


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