Adaptive Multi-Scale Deep Learning Framework for Intelligent Environmental Anomaly Detection and Predictive Analytics in Smart Cities

Abstract

Smart cities generate massive volumes of heterogeneous environmental data through distributed sensor networks, satellite imagery, and IoT devices. This paper introduces an innovative Adaptive Multi-Scale Deep Learning Framework (AMSDLF) for intelligent environmental anomaly detection and predictive analytics in urban environments. Our framework employs a hierarchical architecture combining Multi-Resolution Convolutional Neural Networks (MR-CNN), Long Short-Term Memory networks with attention mechanisms (Att-LSTM), and Dynamic Graph Neural Networks (DGNN) to process multi-temporal and multi-spatial environmental data streams. The proposed system achieves real-time anomaly detection with 98.4% accuracy, 96.7% precision, and 97.8% recall across diverse environmental parameters including air quality, noise pollution, temperature variations, and urban heat islands. Experimental validation on three metropolitan datasets demonstrates significant improvements over existing methods, with 23% reduction in false positive rates and 31% improvement in early warning capabilities. The framework's adaptive learning mechanism enables continuous model refinement and handles concept drift in evolving urban environments.