In particular, ANN has been shown to be effective for more complex tasks. Apart from the rapid economic growth, air pollution is affected by unfavorable meteorological conditions (Al-Saadi et al., 2005 Gong and Ordieres-Meré, 2016 Li et al., 2017b).Īrtificial neural network (ANN) has been performed to predict ground motion (Wiszniowski, 2016), and groundwater depth (He et al., 2014). However, air quality forecasting is quite complex (Li et al., 2017a Park et al., 2018). In 2013, China suffered extremely serious haze pollution, influencing 800 million people, and daily average PM 2.5 concentrations at a site in Xi’an were more than twice those of Beijing, Shanghai, and Guangzhou (Huang et al., 2014).Īir pollution forecasting also is crucial for public health interventions and air pollution control policymaking. The number of haze days in a year has also risen evidently in China, which has seriously hindered the sustainable development of society and caused widespread concern from all walks of life (Jiang and Bai, 2018). And air pollution has adverse effects on people’s life span, and social communication willingness (Huang et al., 2018).ĭue to the large-scale development of industrialization and urbanization, China has been suffering from acute air pollution for many years (Liu and Diamond, 2005). Poor air quality is one of the five major health risks in the world, for example, long-term exposure to polluted air is related to respiratory infections, heart attack, stroke and lung cancer (Kessler, 2014 Watson, 2014 Lelieveld and Pöschl, 2017). Meanwhile, it also has a detrimental effect on visibility, climate, and sustainable development (Lelieveld et al., 2015). Keywords: Air pollution Wavelet artificial neural network Meteorological factor Forecast.Īir pollution is a theme of high importance, and global problems have demonstrated its damaging impacts on human physical health and ecosystems (Nguyen et al., 2015). Thus, our study may provide a theoretical basis for environmental management policies. These results demonstrate that WANNs are effective in short-term API forecasting because they can recognize historic patterns and thereby identify nonlinear relationships between the input and output variables. When Bayesian regularization was applied as a training algorithm, the WANN and ANN models accurately reproduced the APIs in both Xi’an and Lanzhou, although the WANN model (R = 0.8846 for Xi’an and R = 0.8906 for Lanzhou) performed better than the ANN (R = 0.8037 for Xi’an and R = 0.7742 for Lanzhou) during the forecasting stage.
Based on the correlation coefficients between the air pollution index of the targeted day and the tested variables, the API displayed the closest relationship with the API 1 day earlier as well as stronger correlations with the average temperature, average water vapor pressure, minimum temperature, maximum temperature, API 2 days earlier, and API 3 days earlier. Additionally, the API could be accurately predicted based solely on the value recorded 3 days earlier. Evaluating twelve algorithms and nineteen network topologies for the ANN and WANN models, we discovered that the optimal input variables for an API forecasting model were the APIs from the 3 preceding days and sixteen selected meteorological factors.
In this study, we used correlation analysis and artificial neural networks (ANNs including wavelet ANNs ) to identify the linear and nonlinear associations, respectively, between the air pollution index (API) and meteorological variables in Xi’an and Lanzhou. Only including past 3 days’ API as parameters in input datasets gives precise results.Īir quality forecasting is a significant method of protecting public health because it provides early warning of harmful air pollutants.