A feature-centric machine learning framework for forecasting urban air pollution in arid regions

Data-driven air quality prediction in Navi Mumbai city using machine learning techniques

Real-time Nowcasting of Urban Air Quality Index Based on LightGBM and Feature Engineering

The proposed LightGBM model can effectively achieve real-time AQI nowcasting for Hefei City and provides a scientific method for real-time air pollution monitoring and early warning.

A Hyperparameter-Optimized LSTM Machine Learning Method for Predicting Air Quality

The experimental results reveal that the XGBoost model outperforms the other models, while hyperparameter tuning further improves the effectiveness of both the Decision Tree and KNN models.

Estimation And Prediction Of PM2.5 And PM10 Concentration In Kathmandu Valley Using Remote Sensing And Machine Learning

The study demonstrates the potential of integrating satellite data with machine learning for air quality monitoring in data-scarce regions, supporting evidence-based environmental policy in Nepal and similar contexts.

Comparative Performance of Tree-Based Machine Learning Algorithms for Horizontal Visibility Classification at Soekarno–Hatta International Airport Using Meteorological and PM2.5 Data

Low horizontal visibility phenomena are a major threat to aviation safety and operational efficiency, particularly at Soekarno–Hatta International Airport (WIII), which is vulnerable to fog and haze arising from complex interactions between meteorological conditions and air quality. This study develops and comparativel...

Interpretable application of machine learning techniques in the assessment of coastal water quality of Poyang Lake, China

It is demonstrated that interpretable MLMs can effectively reveal the environmental drivers of lake water quality and offer a robust framework for ecosystem monitoring and forecasting in freshwater environments.