Abstract
Air quality monitoring and assessment are essential issues for sustainable environmental protection. The monitoring process is composed of data collection, evaluation, and decision-making. Several important pollutants, such as SO2, CO, PM10, O3, NOx, H2S, location, and many others, have important effects on air quality. Air quality should be recorded and measured based on the total effect of pollutants that are collectively prescribed by a numerical value. In Canada, the Air Quality Health Index (AQHI) is used which is one numerical value based on the total effect of some concentrations. Therefore, evolution is required to consider the complex, ill-defined air pollutants, hence several naive and noble approaches are used to study AQHI. In this study, three approaches such as hybrid data-driven ANN, nonlinear autoregressive with external (exogenous) input (NARX) with a neural network, and adaptive neuro-fuzzy inference (ANFIS) approaches are used for estimating the air quality in an urban area (Jeddah city—industrial zone) for public health concerns. Over three years, 1771 data were collected for pollutants from 1 June 2016 until 30 September 2019. In this study, the Levenberg-Marquardt (LM) approach was employed as an optimization method for ANNs to solve the nonlinear least-squares problems. The NARX employed has a two-layer feed-forward ANN. On the other hand, the back-propagation multi-layer perceptron (BPMLP) algorithm was used with the steepest descent approach to reduce the root mean square error (RMSE). The RMSEs were 4.42, 0.0578, and 5.64 for ANN, NARX, and ANFIS, respectively. Essentially, all RMSEs are very small. The outcomes of approaches were evaluated by fuzzy quality charts and compared statistically with the US-EPA air quality standards. Due to the effectiveness and robustness of artificial intelligent techniques, the public’s early warning will be possible for avoiding the harmful effects of pollution inside the urban areas, which may reduce respiratory and cardiovascular mortalities. Consequently, the stability of air quality models was correlated with the absolute air quality index. The findings showed notable performance of NARX with a neural network, ANN, and ANFIS-based AQHI model for high dimensional data assessment.
Highlights
IntroductionOne of the most critical factors that significantly affect climate change and human health is air pollution
NARX with a neural network, ANFIS, and machine learning are highly interrelated soft computing systems for information processing approaches, and capable of deep learning. They were employed for the big-data advancement of the environmental systems, using the back-propagation multi-layer perceptron (BPMLP), two-layer feedforward ANN algorithm and steepest descent approach to reduce the mean square error of the big data set of training
This study aims to envisage air quality and its distribution using soft computing techniques, such as adaptive neurofuzzy system (ANFIS), and NARX with neural network and ANNs as machine learning approaches
Summary
One of the most critical factors that significantly affect climate change and human health is air pollution. Many countries have been using different systems for monitoring air pollution. This area of research is of interest and very active. Several naive modeling approaches have been presented in the literature are hybrid approaches [1,2], a linear unbiased estimator [3], autoregressive integrated moving average (ARIMA) [4,5] bias adjustment [6], and principal component regression approach. Non-parametric regression [7], artificial intelligence (AI) techniques, machine learning [8], neuro-fuzzy inference systems, and autoregression feedforward ANN with genetic algorithm [9] are noble air quality modeling and control approaches. Simulation and data mining are well-known modeling tools and techniques for predicting and assessing air quality
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
