Application of Artificial Neural Networks in the Prediction of PM10 Levels in the Winter Months: A Case Study in the Tricity Agglomeration, Poland

Nidzgorska-Lencewicz, Jadwiga

doi:10.3390/atmos9060203

Cited by 16 publications

(10 citation statements)

References 45 publications

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“…Due to the good performance of shallow neural networks, such as support vector regression (SVR) [ 13 , 14 ] and artificial neural network (ANN) [ 15 , 16 , 17 , 18 ], many studies applied shallow learning to prediction tasks. Compared with the linear models and time series models, shallow neural networks have stronger performance and better prediction performance for the nonlinear system.…”

Section: Related Workmentioning

confidence: 99%

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

Lin

Zhao

et al. 2022

Entropy

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Accurate and fine-grained prediction of PM2.5 concentration is of great significance for air quality control and human physical and mental health. Traditional approaches, such as time series, recurrent neural networks (RNNs) or graph convolutional networks (GCNs), cannot effectively integrate spatial–temporal and meteorological factors and manage dynamic edge relationships among scattered monitoring stations. In this paper, a spatial–temporal causal convolution network framework, ST-CCN-PM2.5, is proposed. Both the spatial effects of multi-source air pollutants and meteorological factors are considered via spatial attention mechanism. Time-dependent features in causal convolution networks are extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-PM2.5 are tuned by Bayesian optimization. Haikou air monitoring station data are employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results include the following points: (1) For a single station, the RMSE, MAE and R2 values of ST-CCN-PM2.5 decreased by 27.05%, 10.38% and 3.56% on average, respectively. (2) For all stations, ST-CCN-PM2.5 achieve the best performance in win–tie–loss experiments. The numbers of winning stations are 68, 63, and 64 out of 95 stations in RMSE (MSE), MAE, and R2, respectively. In addition, the mean MSE, RMSE and MAE of ST-CCN-PM2.5 are 4.94, 2.17 and 1.31, respectively, and the R2 value is 0.92. (3) Shapley analysis shows wind speed is the most influencing factor in fine-grained PM2.5 concentration prediction. The effects of CO and temperature on PM2.5 prediction are moderately significant. Friedman test under different resampling further confirms the advantage of ST-CCN-PM2.5. The ST-CCN-PM2.5 provides a promising direction for fine-grained PM2.5 prediction.

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Section: Related Workmentioning

confidence: 99%

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

Lin

Zhao

et al. 2022

Entropy

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“…Shallow neural networks, such as support vector regression (SVR) [27,28] and artificial neural network (ANN) [29,30] have been applied to sequential prediction tasks. Motesaddi et al used ANN to study the AQI prediction of SO 2 .…”

Section: Related Workmentioning

confidence: 99%

Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

et al. 2022

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Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R2 was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R2 value was 0.906. (3) Shapley analysis showed PM10, humidity and NO2 were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction.

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“…Yet, PM 10 has a very different source in each country due to different factors (Aldrin & Haff, 2005; Y. Yildirim & Bayramoglu, 2006;Díaz-Robles et al, 2008;Taşpınar, 2015;Nidzgorska-Lencewicz, 2018).…”

Section: Description Of Pm 1mentioning

confidence: 99%

Investigation of PM₁₀ prediction utilizing data mining techniques: Analyze by topic

Srijiranon

Eiamkanitchat

Ramingwong

et al. 2021

WIREs Data Min & Knowl

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Coarse particulate matter (PM10), the inhalable particles with an aerodynamic diameter smaller than 10 micrometers are one of the major air pollutions that affect human health. Over the previous decade, a number of researchers applied various data mining techniques to create a temporal prediction model. This study reviews and discusses 100 research articles in computer science and environmental science coming from the Scopus database. The three processes of data mining techniques, including data preparation, model creation, and model evaluation for prediction PM10 are highlighted. A summary of the overall process directions of data mining as well as their output are revealed. Additionally, recommendations for future research are identified.This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Technologies > Prediction

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Application of Artificial Neural Networks in the Prediction of PM10 Levels in the Winter Months: A Case Study in the Tricity Agglomeration, Poland

Cited by 16 publications

References 45 publications

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

Investigation of PM₁₀ prediction utilizing data mining techniques: Analyze by topic

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Application of Artificial Neural Networks in the Prediction of PM10 Levels in the Winter Months: A Case Study in the Tricity Agglomeration, Poland

Cited by 16 publications

References 45 publications

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

Investigation of PM10 prediction utilizing data mining techniques: Analyze by topic

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Investigation of PM₁₀ prediction utilizing data mining techniques: Analyze by topic