Optimal machine learning techniques to predict air quality index

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

Overcoming data shortages through hybrid machine learning in air quality forecasting: a case study of Kağıthane, Istanbul

A hybrid machine-learning framework for AQI prediction in Kağıthane, Istanbul, Türkiye, using a long-term dataset spanning approximately ten years, thereby reducing reliance on dense sensor infrastructures and achieving overall accuracy of approximately 98%, enabling timely health advisories and more efficient allocati...

Artificial Intelligence for Air Quality Prediction: A Review of Machine Learning and Deep Learning Applications in Atmospheric Pollution Forecasting

Air pollution is a major global environmental and public health challenge, causing respiratory diseases, environmental degradation, and premature mortality. Accurate air quality prediction is essential for environmental management, reducing human exposure to harmful pollutants, and supporting timely mitigation strategi...

Deep neural network-based prediction of air quality index (AQI) for municipal corporation station, Solapur, Maharashtra State, India

The efficiency of the proposed DNN model is proved, and the comparative analysis with the baseline models, such as Linear Regression and Support Vector Regression, demonstrates that the proposed model is more effective than traditional techniques in identifying nonlinear dependencies in air pollution data.

Explainable Machine Learning for Air Quality Prediction, With Satellite and Weather Data

Air pollution is a major threat in current times and I consider PM2.5 as a pollutant because PM2.5 has negative impacts on human health. Hence PM2.5 prediction is crucial. The prediction of PM2.5 is a challenging task due to its non-linear relationship with various environmental factors and dust storm events. In this p...

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.