Explainable Artificial Intelligence (XAI) for Air Quality Assessment

Abstract

Accurate air quality analysis is essential for comprehending the reasons for and consequences of air pollution, which is a serious environmental concern. Understanding the underlying causes contributing to pollution levels is challenging when using traditional methodologies for air quality analysis since they frequently lack transparency and interpretability. This work examines the integration of XAI with deep learning to enhance air quality prediction. Explainable AI provides a solution by illuminating the ways in which AI models make decisions. It emphasizes the requirement for clear and understandable AI models to win stakeholders' trust and adoption. Utilizing explainable AI makes it feasible to improve the readability and transparency of air quality studies, allowing stakeholders to comprehend and verify the predictions and suggestions made by AI systems. Rough woodland. To classify the data, XGBoost and KNN are used. SHAP and LIME are then applied to discover the major characteristics and variables that affect air quality predictions. These findings can help to improve decision-making and the creation of efficient plans for the management and mitigation of air quality.