Air Quality Prediction

About

Air quality prediction is the scientific process of forecasting the concentrations of various atmospheric pollutants at specific future times and locations. This academic field and methodological approach involves the development and application of computational models and data-driven techniques to estimate future air quality conditions, incorporating factors such as meteorological patterns, emission sources, and chemical transformations, thereby providing essential information for environmental management, public health protection, and policy development.

Top Publications

Rankings shown are based on citation count.

Machine learning methods for better water quality prediction	2019
A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring	2018
Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data	2019
PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data	2019
Real-time air quality forecasting, part I: History, techniques, and current status	2012