ENVIRONMENTAL IMPACT ASSESSMENT USING ARTIFICIAL INTELLIGENCE AND GIS: A DATA-DRIVEN FRAMEWORK FOR SUSTAINABLE ENVIRONMENTAL MANAGEMENT

Abstract

Environmental Impact Assessment (EIA) is a critical process for evaluating the environmental consequences of development projects before implementation. Traditional EIA approaches often rely on manual data analysis, field surveys, and expert judgment, which may lead to uncertainties and time-consuming evaluations. Recent advancements in Artificial Intelligence (AI) and Geographic Information Systems (GIS) have significantly improved the accuracy, efficiency, and spatial analysis capabilities of environmental assessments. This study explores the integration of AI and GIS technologies for enhanced Environmental Impact Assessment. The research examines machine learning models, spatial analysis techniques, and remote sensing tools used to evaluate environmental risks and predict ecological changes. A conceptual framework for AI-GIS-based EIA is proposed, highlighting data integration, predictive modeling, and decision-support systems. Results indicate that AI-based predictive models can improve environmental risk forecasting accuracy by over 85–90%, while GIS enables spatial visualization of environmental changes and impact zones. The integration of geospatial datasets, remote sensing, and AI algorithms provides decision-makers with real-time environmental monitoring and scenario simulations. Emerging trends include GeoAI, autonomous GIS systems, IoT-based environmental monitoring, and digital twin modeling. However, challenges such as data heterogeneity, model uncertainty, and ethical concerns remain significant. Future research should focus on explainable AI models, integrated environmental data platforms, and policy frameworks for AI-driven environmental governance. This study concludes that AI-GIS integration represents a transformative approach for sustainable environmental planning and impact assessment.