Work

Comparative Evaluation of Machine Learning Models for Urban Air Quality Forecasting in Emerging Economies

Year: 2026

Type: article

Abstract: This study undertakes a comparative evaluation of Random Forest (RF) and XGBoost for daily Air Quality Index (AQI) forecasting across five major Indian metropolitan cities—Delhi, Mumbai, Kolkata, Chennai and Bangalore—spanning 2021 to 2025. The dataset comprises 7,605 city-day observations drawn from the CPCB monitoring archive, enriched with temporal lag features, rolling window statistics and cyclical calendar encodings. Both models were trained on an 80/20 temporal split and evaluated using RMSE, MAE and R². RF demonstrated superior predictive accuracy across all five cities, yielding an overall R²=0.6565 versus 0.6390 for XGBoost, with peak performance in Mumbai (R²=0.8627) and Delhi (R²=0.8516). Feature importance analysis confirmed the primacy of lagged AQI values (AQI_lag1: 86.34% of RF importance), underscoring the strongly autoregressive nature of urban air quality dynamics. Seasonal analysis identified Winter as the highest-pollution season (mean AQI=161.75) and the most challenging for accurate prediction. Findings provide actionable guidance for data-driven early-warning systems in emerging economy urban contexts.

Source: Iconic Research and Engineering Journals

DOI: 10.64388/irev9i11-1717704

Authors: Moksha Vora, Dr. Syed Shahid Raza

Institution: Jain University

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure, Climate action, Good health and well-being

Open Access status: bronze

OpenAlex ID: W7160962964