Air Quality Prediction Through Regression ModelExamining and protecting air quality in this world has become one of the essential activities for every human in many industrial and urban areas today. The meteorological and traffic factors, burning of fossil fuels, and industrial parameters play ...
resolution Chinese air quality reanalysis datasets (CAQRA) has been developed by assimilating over 1000 surface air quality monitoring sites from China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS)...
Specifically, we develop a prediction model by combining multitask learning techniques with recurrent neural network (RNN) models and perform empirical analyses to evaluate the utility of each facet of the proposed framework based on a real-world dataset that contains 451,509 air quality records ...
temperature, and humidity data; (2) we simultaneously take images covering the locations of the particle counters; and (3) we evaluate several vision-based state-of-art PM concentration prediction algorithms on our dataset and demonstrate that prediction accuracy increases with sensor density and imag...
To optimize the prediction model, the variable importance in the projection (VIP) of the partial least squares (PLS) is used to select key input variables as a preprocessing step. The prediction models are applied to a real indoor air quality dataset from telemonitoring systems data (TMS), ...
In recent years, A series of environmental problems caused by air pollution have attracted widespread attention. Air quality forecasting has become an indispensable part of people’s daily life. However, the traditional air quality prediction (AQP) model
One algorithm proposed in this paper is a two-layer model prediction algorithm based on Long Short Term Memory Neural Network and Gated Recurrent Unit(LSTM&GRU). This algorithm is an improvement and enhancement of the existing prediction method Long Short Term Memory. The Recurrent Neural Network ...
Essentially, the physics-inspired hybrid deep graph network is built on a flexible and unified architecture of air pollutant emissions, pollutant transport, and deposition, and is designed for spatiotemporal prediction. Machine learning has played a crucial role in improving air quality assessments over...
The prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city’s economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Per...
python ./experiments/airformer/main.py --mode test --gpu 0 --dataset AIR_TINY --stochastic_flag False If you find our work useful in your research, please cite: @article{liang2022airformer, title={AirFormer: Predicting Nationwide Air Quality in China with Transformers}, author={Liang, Yuxu...