Smart Earth: A Deep Learning-Powered Approach For Real-Time Climate Analysis And Ecosystem Forecasting

Abstract

This research introduces Smart Earth, a deep learning-based experimental framework for real-time climate analysis and environmental forecasting. Traditional models often fail to capture climate systems' complex, dynamic nature. Smart Earth addresses this by combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks with an attention mechanism to extract both spatial and temporal features from diverse climate data sources. The model is trained on a comprehensive dataset that includes satellite imagery, historical weather records, and sensor-based environmental readings. In experiments conducted across various climate-sensitive regions, Smart Earth achieved a forecasting accuracy ranging from 96.4% to 96.8%, surpassing traditional statistical models and recent AI approaches. It effectively predicts extreme climate events like droughts and heatwaves while offering visual interpretability through attention heatmaps. Smart Earth demonstrates the powerful potential of deep learning in enhancing environmental intelligence and provides a reliable decision-support tool for scientists, environmental planners, and policymakers.