Comparative study of two different prediction models for winter AOD

Wang, Jianwei; Xiaohui, Chu; Feng, Xiaoyi; Liu, Weiliang; Jin, Ma

doi:10.1109/icicip.2017.8113914

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of shallow machine learning, a variant of the support vector machine model, the support vector regression model (SVR), is often used for the task of time series prediction. Wang et al [15] compared the support vector regression model with a Back Propagation neural network(BP) for PM2.5 prediction and analyzed that the support vector regression model was superior to the BP in air quality prediction. Lijie Dai et al [16] proposed an air quality prediction model fusing support vector machine and particle swarm algorithm to predict PM2.5 in Shanghai for 24 h. However, the support vector machine model seriously affects its prediction performance due to the problem of high computational complexity and excessive computational effort when facing massive data.…”

Section: Literature Reviewmentioning

confidence: 99%

Hour-by-Hour Prediction Model of Air Pollutant Concentration Based on EIDW-Informer—A Case Study of Taiyuan

Lai

Sheng

et al. 2023

Atmosphere

View full text Add to dashboard Cite

Prediction of air pollutant concentrations is currently one of the most important methods for the prevention and control of urban air pollution in most countries, and accurate and timely prediction of pollutant concentrations is of great significance for urban pollution control. Using Taiyuan, China, as a case study, this study examines how to predict hourly air pollutant concentrations over longer periods of time while ensuring their accuracy. In this paper, an air pollutant concentration prediction method based on improved inverse distance interpolation and Informer model (EIDW-Informer), and hour-by-hour prediction of PM2.5, NO2, and O3 concentrations in Taiyuan, China is carried out. In this study, historical data from seven environmental monitoring stations in Taiyuan City were used to build multidimensional environmental vectors and calculate the similarity between sample points. Then, the missing values in the dataset were interpolated according to the similarity and distance weights, and the long series prediction was performed by Informer. The experimental results show that the EIDW-Informer method has advantages in hour-by-hour prediction compared to LSTM, CNN-LSTM, and Attention-LSTM models, which improves by 20%, 27%, and 43% on 1 h, 8 h, and 72 h time scales, respectively.

show abstract

Section: Literature Reviewmentioning

confidence: 99%