AI-driven Environmental Impact Assessment Models for Urban Development: A Review and Future Directions

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IJEP 45(7): 652-660 : Vol. 45 Issue. 7 (July 2025)

Full Text

Minhaj Abdullah*

Mahindra University, School of Digital Media and Communication, Hyderabad – 500 043, Telangana, India

Abstract

This paper offers a thorough review of how artificial intelligence (AI) models are integrated into environmental impact assessments (EIAs) to improve environmental management in urban development. Conventional EIA approaches are often hindered by being labour-intensive and subjective, frequently overlooking the complex interactions within urban ecosystems. To address these shortcomings, AI-enhanced EIA models provide greater efficiency, accuracy and flexibility by utilizing machine learning, deep learning and agent-based modelling techniques. The research identifies important AI applications, such as neural networks, support vector machines and random forest algorithms, which boost predictive precision in evaluating air and water quality, biodiversity effects and changes in landuse. The paper recommends an AI-oriented EIA framework that includes data gathering, preprocessing, model selection, training and real-time visualization via interactive maps and dashboards. This framework supports urban planners and policymakers by enhancing mitigation strategies and assessing the cost-effectiveness of environmental safeguards. Despite the improvements AI brings to the reliability of EIAs, challenges, such as data quality, transparency of models and ethical issues surrounding algorithmic decision-making remain. The paper emphasizes the promise of future developments in AI technologies to further improve EIAs, focusing on real-time monitoring, involvement of stakeholders and responsive actions to urban environmental issues. This study highlights the transformative impact of AI on promoting sustainable urban development by enabling data-driven, transparent and effective measures for environmental protection.

Keywords

Artificial intelligence, Environmental impact assessment, Urban development, Machine learning, Deep learning, AI-driven environmental impact assessment, Agent-based modelling, Urban planning, Predictive modelling, Environmental mitigation, Data-driven decision-making

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