KaydedilenlerDerginizi ekleyin

Dergiler / Atmospheric Pollution Research / 2020 / Cilt: 11 - Sayı: 6

Prediction of ozone hourly concentrations by support vector machine and kernel extreme learning machine using wavelet transformation and partial least squares methods

Xiaoqian SU
Junlin AN
Guo Ping ZHU
Bin ZHU
Yuxin ZHANG
Sayfa
51–60
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The KELM-WT-PLS model shows the best fit of the O3 hourly concentration, and the corresponding MAE, MAPE, RMSE, NRMSE and R2 are 7.71 ppb, 0.37, 9.75 ppb, 11.83% and 0.78, while KELM predict the O3 hourly concentration more accurately.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In this paper, we develop a method for predicting ozone (O3) concentration based on kernel extreme learning machine (KELM) and support vector machine regression (SVR) and pretreat it by wavelet transformation (WT) and partial least squares (PLS).
📊
Temel Bulgular & Veri:To test the method's effectiveness, the observation (2014–2016 summer) of the precursors, meteorology and hourly O3 concentrations in the Nanjing industrial zone were applied.
💡
Sonuç & Katkı:The KELM-WT-PLS model shows the best fit of the O3 hourly concentration, and the corresponding MAE, MAPE, RMSE, NRMSE and R2 are 7.71 ppb, 0.37, 9.75 ppb, 11.83% and 0.78, while KELM predict the O3 hourly concentration more accurately.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2014–2016 Verileri
⚙️Değişkenler:Alan Parametreleri

Özet

In this paper, we develop a method for predicting ozone (O3) concentration based on kernel extreme learning machine (KELM) and support vector machine regression (SVR) and pretreat it by wavelet transformation (WT) and partial least squares (PLS). To test the method's effectiveness, the observation (2014–2016 summer) of the precursors, meteorology and hourly O3 concentrations in the Nanjing industrial zone were applied. The mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R2) were chosen to evaluate the model. Results demonstrate that the KELM and SVR perform better than stepwise regression (SR) methods and back propagation neural network (BPNN) for predicting O3 concentration. WT decomposes the original time series of O3 concentration into a few sub–series with less variability, and then improve the performance of SVR and KELM by 16.99%~30.91% and 16.00%~25.86%, respectively. The variable importance in projection (VIP) value was used to filter the influence factors of each sub–sequence, which can remove redundant information and reduce the calculation amount of the model. In addition, the WT and PLS methods enhance the predictive abilities of KELM and SVR for higher O3 concentrations by 21% and 35% respectively. The KELM-WT-PLS model shows the best fit of the O3 hourly concentration, and the corresponding MAE, MAPE, RMSE, NRMSE and R2 are 7.71 ppb, 0.37, 9.75 ppb, 11.83% and 0.78, while KELM predict the O3 hourly concentration more accurately.

🔖

Bu Makaleye Atıf Yap

SU, X., AN, J., ZHU, G. P., ZHU, B., ZHANG, Y. (2020). Prediction of ozone hourly concentrations by support vector machine and kernel extreme learning machine using wavelet transformation and partial least squares methods. Atmospheric Pollution Research, Cilt: 11 - Sayı: 6, 51–60. https://ajindex.com/articles/144454

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (9)

  1. An, J.L., Wang, Y.S., 2010. Principal component and multiple regression analysis pre- dicting ozone concentrations: case study in summer in Beijing. Acta Sci. Circumstantiae 30, 1286–1294 (in Chinese). An, J.L., Zhu, B., Wang, H.L., Li, Y.Y., Lin, X., Yang, H., 2014. Characteristics and source apportionment of VOCs measured in an industrial area of Nanjing, Yangtze River Delta, China. Atmos. Environ. 97, 206–214.
  2. An, J.L., Zou, J.N., Wang, J.X., Lin, X., Zhu, B., 2015. Differences in ozone photochemical characteristics between the megacity Nanjing and its suburban surroundings, Yangtze River Delta, China. Environ. Sci. Pollut. Res. 22, 19607–19617.
  3. Bai, Y., Li, Y., Wang, X.X., Xie, J.J., Li, C., 2016. Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with me- teorological conditions. Atmos. Pollut. Res. 7, 557–566.
  4. Borrego, C., Tchepel, O., Costa, A.M., Amorim, J.H., Miranda, A.I., 2003. Emission and dispersion modelling of Lisbon air quality at local scale. Atmos. Environ. 37, 5197–5205.
  5. Canu, S., Rakotomamonjy, A., 2001. Ozone Peak and Pollution Forecasting Using Support Vectors. IFAC Workshop on Environmental Modeling, Yokohama, pp. 22–23.
  6. Chelani, A.B., 2010. Prediction of daily maximum ground ozone concentration using support vector machine. Environ. Monit. Assess. 162, 169–176.
  7. Chen, Y.L., Zhao, Z.J., 2013. A multi–step–ahead prediction of ozone concentration using wavelet transform and traditional time series model. Acta Sci. Circumstantiae 33, 339–345 (in Chinese). Ding, S., Chen, B.Z., Wang, J., Chen, L., Zhang, C.L., Sun, S.B., Huang, C.W., 2018. An applied research of decision–tree based statistical model in forecasting the spa- tial–temporal distribution of O3. Acta Sci. Circumstantiae 38, 3229–3242 (in Chinese). Duncan, B.N., Lamsal, L.N., Thompson, A.M., Yoshida, Y., Lu, Z.F., Streets, D.G., Hurwitz, M.M., Pickering, K.E., 2016. A space–based, high–resolution view of notable changes in urban NOx pollution around the world (2005–2014). J. Geophys. Res. Atmos. 121, 976–996.
  8. Dunea, D., Pohoata, A., Iordache, S., 2015. Using wavelet–feedforward neural networks to improve air pollution forecasting in urban environments. Environ. Monit. Assess. 187, 477. Farajzadeh, J., Alizadeh, F., 2017. A hybrid linear–nonlinear approach to predict the monthly rainfall over the Urmia Lake watershed using wavelet–SARIMAX–LSSVM conjugated model. J. Hydroinf. 20, 246–262.
  9. Feng, R., Zheng, H.J., Zhang, A.R