Air quality data series estimation based on machine learning approaches for urban environments
- PDF / 1,833,864 Bytes
- 11 Pages / 595.276 x 790.866 pts Page_size
- 83 Downloads / 176 Views
Air quality data series estimation based on machine learning approaches for urban environments Alireza Rahimpour 1 & Jamil Amanollahi 1,2 & Chris G. Tzanis 3 Received: 11 June 2020 / Accepted: 26 August 2020 # Springer Nature B.V. 2020
Abstract Air pollution is one of the main environmental problems in residential areas. In many cases, the effects of air pollution on human health can be prevented by forecasting the air quality in the next day. In order to predict the 1 day in advance air quality index (AQI) of Orumiyeh city, the hybrid single decomposition (HSD) and hybrid two-phase decomposition (HTPD) models were used. In the first step, the AQI data were decomposed by complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and was hybridized with general regression neural network (GRNN) and extreme learning machine (ELM) as HSD models. In the second step, using variational mode decomposition (VMD) technique the results of the first intrinsic mode functions (IMFs) of CEEMDAN model were decomposed into nine VMs and were predicted by GRNN and ELM models to obtain IMF1. Finally, in the third step, GRNN and ELM were used again to predict the IMFS as HTPD models. Results showed that in predicting AQI series data by HSD models both CEEMDAN-ELM and CEEMDAN-GRNN models were similarly accurate. Among all the models used, the accuracy of CEEMDAN-VMD-GRNN as the HTPD model was the highest in the training phase (R2 = 0.98, RMSE = 4.13 and MAE = 2.99) and in the testing phase (R2 = 0.74, RMSE = 5.45 and MAE = 3.87). It can be concluded that HTPD models have more accurate results to predict AQI data compared with HSD models. Keywords Hybrid model . Intrinsic mode functions . Train . CEEMDAN-GRNN . CEEMDAN-VMD-GRNN
Introduction Background Air pollution is known to be the culprit behind a variety of health problems, such as chronic bronchitis in adults, acute respiratory
* Jamil Amanollahi [email protected] Alireza Rahimpour [email protected] Chris G. Tzanis [email protected] 1
Department of Environmental Sciences, Faculty of Natural Resources, University of Kurdistan, Pasdaran Street, P.O.Box 416, Sanandaj, Iran
2
Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj, Iran
3
Climate and Climatic Change Group, Section of Environmental Physics and Meteorology, Department of Physics, National and Kapodistrian University of Athens, University Campus, Bldg Phys-5, 15784 Athens, Greece
infections in children, lung cancer, and heart diseases. Pre-existing lung and heart diseases have been proven to be aggravated by air pollution which can result in asthmatic attacks (Kampa and Castanas 2008; Singh et al. 2019). Atmospheric pollution also has a variety of other effects (Amanollahi et al. 2016; Ganguly and Tzanis 2011; Chen et al. 2019). When it comes in high concentrations, the suspended particulate matter canaffect regional climate (Tzanis and Varotsos 2008; Simaha et al. 2013), monsoonal rainfall (Niyogi et al. 2007). and mortal
Data Loading...
