Paper Contents
Abstract
AI-driven learning techniques offer powerful capabilities for fine-grained air qualityinterpolation, prediction, and feature analysis. By integrating heterogeneous data sourcessuch as air quality sensors, meteorological inputs, satellite imagery, traffic patterns, andenvironmental factors, advanced machine learning models can accurately estimate missingvalues and forecast future pollution levels. These models effectively capture complex spatialand temporal dependencies, enabling more precise assessments across diverse urban andregional settings. Additionally, interpretability methods like SHAP values or attentionmechanisms help identify key contributing factors, offering valuable insights forenvironmental monitoring, urban planning, and public health decision-making
Copyright
Copyright © 2025 Kumar R, Nandan C, Vishwanatha R, Chaitra S P. This is an open access article distributed under the Creative Commons Attribution License.
