Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions

Abstract

Air quality forecasting is a significant method of protecting public health because it provides early warning of harmful air pollutants. In this study, we used correlation analysis and artificial neural networks (ANNs; including wavelet ANNs [WANNs]) to identify the linear and nonlinear associations, respectively, between the air pollution index (API) and meteorological variables in Xi'an and Lanzhou. 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. Additionally, the API could be accurately predicted based solely on the value recorded 3 days earlier. 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. 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. 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. Thus, our study may provide a theoretical basis for environmental management policies.

Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence

“…Artificial intelligence (AI) has been proven of their feasibility in capturing nonlinear relationships. Artificial intelligence has been widely used in many fields (Cassimon et al 2020;Guo et al 2020;He et al 2014), and AI can quickly diagnose COVID-19 (Zhang et al 2020). To overcome limitations of the epidemiological model, we develop artificial intelligence (AI) for real-time predicting of the new confirmed cases of COVID-19 all over the world.…”

Air quality prediction models based on meteorological factors and real-time data of industrial waste gas

“…Ye’s analysis of Fairbanks confirmed that air temperature and AQI were positively correlated, while visibility was negatively correlated with AQI 28 . Guo studied the correlation between meteorological factors and AQI and also verified that there was a positive correlation between temperature and AQI 29 . These are consistent with the correlation analysis results obtained in this paper.…”

Comparative Evaluation of the Multilayer Perceptron Approach with Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death Cases

“…An information-driven way to deal with displaying COVID-19 has likewise arisen, in which measurable and machine learning models are utilized for gauging cases, hospitalizations, passings, and effects of social separating [ 26 , 27 ]. Considering machine learning approaches, forecasting by using artificial and wavelet neural networks with meteorological conditions has been studied by Guo et al [ 28 ]. Guo and He [ 29 ] predicted confirmed death cases together with confirmed global COVID-19 confirmed cases utilizing artificial intelligence.…”