Improving the Accuracy of Forecasting PM2.5 Concentrations With Hybrid Neural Network Model

Thanavanich, Thanawut; Yaibuates, Mayoon; Suchaya, Pheeradon

doi:10.1109/ectidamtncon51128.2021.9425724

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The previous works studied the hybrid technique includes cluster-based [31], ANN and multiple linear regression [32], ANN and k-means clustering [33], ANN and wavelet [14], back propagation ANN (BPANN) and wavelet [34], BPANN and adaptive differential evolution [27], deep learning and wavelet [34], recurrent neural network (RNN) and long short-term memory (LSTM) [35], multi-objective Harris hawk's optimization (MOHHO) [36]. The hybrid models can help to enhance the accuracy of PM2.5 prediction and the model also achieve the limitation of single-site prediction to generalized [37]. The hybrid approach [38] can also be applied to forecasting complex problems in other fields.…”

Section: Introductionmentioning

confidence: 99%

Hybrid algorithms based on historical accuracy for forecasting particulate matter concentrations

Thanavanich

Yaibuates

Duangtang

et al. 2022

IJ-AI

Self Cite

View full text Add to dashboard Cite

Air pollution has become one of the most significant problems impacting human health. Particulate matter (PM) 2.5 is usually used as an identifier of the intensity of the pollution. The PM2.5 forecasting is essential and gainful for reducing health risks. The efficient model for forecasting PM2.5 concentration can be used in determining the period of outdoor activities, thereby reducing the impact on health. In addition, the government sector can use the forecasting model as a tool for laying down measures a burning control. In this work, the hybrid forecasting algorithms for improving accuracy are presented. The hybrid forecasting algorithms combine neural network models with historical predictive data for improving the accuracy of forecasting. The experimental results show that the proposed algorithms can reduce the mean absolute error and root mean square error of forecasting at 36% and 45%. Therefore, the proposed algorithms are not only effectively used to forecast the PM2.5 concentrations but also apply the lightweight technique based on historical accuracy to forecast other complex problems efficiently.

show abstract

Section: Introductionmentioning

confidence: 99%

Hybrid algorithms based on historical accuracy for forecasting particulate matter concentrations

Thanavanich

Yaibuates

Duangtang

et al. 2022

IJ-AI

Self Cite

View full text Add to dashboard Cite

show abstract

Machine learning algorithms to forecast air quality: a survey

2023

View full text Add to dashboard Cite

Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011–2021. We have searched the main scientific publications databases and, after a careful selection, we have considered a total of 155 papers. The papers are classified in terms of geographical distribution, predicted values, predictor variables, evaluation metrics and Machine Learning model.

show abstract

Improving the Accuracy of Forecasting PM2.5 Concentrations With Hybrid Neural Network Model

Cited by 2 publications

References 16 publications

Hybrid algorithms based on historical accuracy for forecasting particulate matter concentrations

Hybrid algorithms based on historical accuracy for forecasting particulate matter concentrations

Machine learning algorithms to forecast air quality: a survey

Contact Info

Product

Resources

About