Enhancing PM2.5 prediction by mitigating annual data drift using wrapped loss and neural networks
Sign Up to like & getrecommendations! Published in 2025 at "PLOS ONE"
DOI: 10.1371/journal.pone.0314327
Abstract: In many deep learning tasks, it is assumed that the data used in the training process is sampled from the same distribution. However, this may not be accurate for data collected from different contexts or… read more here.
Keywords: loss; pm2 prediction; model; annual data ... See more keywords
PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data
Sign Up to like & getrecommendations! Published in 2019 at "Atmosphere"
DOI: 10.3390/atmos10070373
Abstract: In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular… read more here.
Keywords: deep learning; pm2 prediction; random forest; pm2 ... See more keywords
An Improved Deep Learning Approach Considering Spatiotemporal Heterogeneity for PM2.5 Prediction: A Case Study of Xinjiang, China
Sign Up to like & getrecommendations! Published in 2024 at "Atmosphere"
DOI: 10.3390/atmos15040460
Abstract: Prediction of fine particulate matter with particle size less than 2.5 µm (PM2.5) is an important component of atmospheric pollution warning and control management. In this study, we propose a deep learning model, namely, a… read more here.
Keywords: pm2; deep learning; pm2 prediction; prediction ... See more keywords