Evaluating Visibility Range on Air Pollution using NARX Neural Network

Abstract

Evaluating air visibility range is considered as one of the apparent criteria of air quality. Haze air as a conclusion of air pollution causes unpleasant breathing, psychological effects, and visibility restriction. In this study, NARX neural network applied to determine air visibility restriction factors. Data of air quality control stations of Baghshomal, Rastebazar, and Abresan in Tabriz City, Iran used which include PM2.5, PM10, NO2, SO2, O3, and CO for the duration of four years from 2013 to 2017 that considered as independent variables. NARX neural network created to find each pollutant relation to visibility restriction and networks used for simulation to analysis network results in conspectuses condition. The results showed that PM10 pollutant has the most influence on-air visibility with R=0.9 in the train, R=0.728 in the test, and R=0.75 in validation process. Also error results of the PM10 obtained as MSE=0.054. Moreover, simulation results demonstrated the least area integral between curves according to ascending order for six pollutant factors and verified PM10 accuracy in NARX network simulation. The total result as study conclusion verified NARX neural network efficiency to evaluate air visibility range while using air pollutant parameters.

Relationship between Visibility, Air Pollution Index and Annual Mortality Rate in Association with the Occurrence of Rainfall—A Probabilistic Approach

“…For example, for the spring period and Vis = 16-20 km, the API value for wet weather is equal to 3.74 and for dry weather, it amounts to 4.66. The results obtained in this study are confirmed by the analysis conducted by Irani et al [39], who, using the artificial neural network method, modeled the values of selected air quality indices (PM10, NO2, SO2, CO, O3) as input data for air quality index calculations also taking into account the amount of rainfall, wind speed and temperature.…”

Enhancing PM2.5 Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model

“…NARX can also predict more steps in the future by using the predicted step and reinserting it into the mapping function to get the next predicted step. NARX has been used by researchers in air-quality prediction [20], evaluating visibility range on air pollution [30], glucose level prediction [27], and data calibration [31]. The main advantage of NARX is that any nonlinear regression function can be used to perform regression on timeseries problems and that there is flexibility in choosing how much history to use.…”

Temporal and Spatial Characteristics of Meteorological Elements in the Vertical Direction at Airports and Hourly Airport Visibility Prediction by Artificial Intelligence Methods

“…Moreover, such human activity factors with obvious time characteristics should be taken into account in the input data of the model, such as the changes in visibility caused by the massive burning of fossil fuels for heating in northern China in cold seasons [43]. Industry, energy consumption, vehicles and other socioeconomic factors are significantly associated with atmospheric aerosols [44][45][46][47], and the increase in aerosols will directly lead to the decrease of visibility. Therefore, in the future model construction, the environmental factors that affect visibility and changed by human activities mentioned in the previous study, such as PM2.5, PM10, NO 2 , SO 2 , O 3 and CO, should be considered at the same time to improve the prediction accuracy of the model.…”