Environmental Impact Prediction Model: Predicting Local Environmental Changes Using Satellite Data and Sensor Feeds to Aid Conservation Efforts

Rapid urbanization, industrial activities, and unsustainable land use have intensified environmental issues such as air pollution and deforestation, leading to biodiversity loss and health risks. Traditional environmental monitoring approaches are often reactive, delayed, and spatially limited. Therefore, developing a real-time predictive framework that combines multiple data sources is essential for timely interventions and sustainable environmental management.
This study introduces a comprehensive Environmental Impact Prediction Model that integrates satellite remote sensing data and ground-based sensor feeds. Sentinel-2 and MODIS satellites provided NDVI, LST, and AOD data, while 12 on-site sensors collected air quality metrics including PM2.5, NO2, and CO2. Preprocessing steps such as cloud masking, normalization, and temporal alignment ensured data quality. LSTM neural networks were applied for air quality forecasting, and Random Forest algorithms were used for deforestation classification. Visual outputs were presented via dynamic geospatial dashboards developed with Python (Dash, Plotly).
The model demonstrated high performance: LSTM-based air quality predictions achieved a Mean Absolute Error of 4.2 AQI units and R² of 0.88. Deforestation detection using Random Forest showed 91% accuracy and 89% precision. The system identified early warning signals for both pollution peaks and forest degradation before they were confirmed by drone inspections and sensor validation, proving its reliability and responsiveness.