A Novel And Efficient Ai Driven Geospatial Simulation For Enhancing The Green Environment In Urban Areas

Abstract

Rapid Urban Growth Has Intensified Environmental Degradation, Particularly In The Form Of Declining Air Quality And Shrinking Green Spaces. Unregulated Development, Population Density Increases, And Industrial Expansion Have Disrupted Ecological Balance And Elevated Atmospheric Pollution Levels. To Address These Challenges, This Study Presents An AI-driven Geospatial Simulation Framework Designed To Analyze Urban Growth Dynamics And Forecast Pollution Severity. The Model Integrates Satellite Imagery, GIS-based Datasets, And Multiple Environmental Parameters To Examine Spatial Transformations In Metropolitan Areas. A Hybrid Ensemble Strategy Combining XGBoost And AdaBoost Is Utilized To Enhance Predictive Robustness And Model Efficiency. The Framework Incorporates Both Spatial And Temporal Features To Categorize City Zones According To Pollution Intensity. Additionally, A Flask-based Backend Supports Real-time Analysis And Interactive User Access. The System Identifies Vulnerable Pollution Hotspots, Projects Future Environmental Risks, And Visualizes Outcomes Using Geospatial Mapping Techniques. By Enabling Evidence-based Urban Planning And Environmental Policy Decisions, The Proposed Framework Provides A Scalable And Intelligent Solution For Sustainable City Development.

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