Prediction of airborne pollen concentrations by artificial neural network and their relationship with meteorological parameters and air pollutants

2022 ◽  
Author(s):  
Gholamreza Goudarzi ◽  
Yaser Tahmasebi Birgani ◽  
Mohammad-Ali Assarehzadegan ◽  
Abdolkazem Neisi ◽  
Maryam Dastoorpoor ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Airborne Pollen ◽  
Pollen Concentrations ◽  
Artificial Neural

scholarly journals Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mahrokh Jalili ◽  
Mohammad Hassan Ehrampoush ◽  
Mehdi Mokhtari ◽  
Ali Asghar Ebrahimi ◽  
Faezeh Mazidi ◽  
...  
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Time Series ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Ambient Air ◽  
Multivariate Linear Regression ◽  
Artificial Neural

AbstractThis study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 years, the mean concentrations of PM10, SO2, O3, NO2, and CO were 98.48 μg m−3, 8.57 ppm, 19.66 ppm, 18.14 ppm, and 4.07 ppm, respectively. The total number of cardiovascular disease (CD) patients was 12,491, of which 57% and 43% were related to men and women, respectively. The maximum correlation of air pollutants was observed between CO and PM10 (R = 0.62). The presence of SO2 and NO2 can be dependent on meteorological parameters (R = 0.48). Despite there was a positive correlation between age and CD (p = 0.001), the highest correlation was detected between SO2 and CD (R = 0.4). The annual variation trend of SO2, NO2, and CO concentrations was more similar to the variations trend in meteorological parameters. Moreover, the temperature had also been an effective factor in the O3 variation rate at lag = 0. On the other hand, SO2 has been the most effective contaminant in CD patient admissions in hospitals (R = 0.45). In the monthly database classification, SO2 and NO2 were the most prominent factors in the CD (R = 0.5). The multivariate linear regression model also showed that CO and SO2 were significant contaminants in the number of hospital admissions (R = 0.46, p = 0.001) that both pollutants were a function of air temperature (p = 0.002). In the ANN nonlinear model, the 14, 12, 10, and 13 neurons in the hidden layer were formed the best structure for PM, NO2, O3, and SO2, respectively. Thus, the Rall rate for these structures was 0.78–0.83. In these structures, according to the autocorrelation of error in lag = 0, the series are stationary, which makes it possible to predict using this model. According to the results, the artificial neural network had a good ability to predict the relationship between the effect of air pollutants on the CD in a 5 years' time series.

scholarly journals Prediction of the Pollutants Concentration Using Artificial Neural Network (ANN)

2015 ◽  
Vol 16 (SE) ◽  
pp. 171-180
Author(s):  
Ahmad Mousavian ◽  
Hady Zarei Mahmodabady ◽  
Aboutaleb Ghadami Jadval Ghadam
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Artificial Neural Network ◽  
Air Pollutants ◽  
Environmental Issues ◽  
Urban Transport ◽  
Regression Methods ◽  
Tangent Function ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Air pollution is one of the most important environmental issues that annual causes to mortality large number of people around the world. So, investigating, measuring, and predicting the concentrations of different pollutants in various areas play an important role in preventing the production of this pollutant sand planning to reduce them by people and relevant authorities. One of the new models that play an important role in measuring and predicting pollution is artificial neural network or regression methods. Therefore, this study is trying to predict air pollution in Yasouj by using artificial neural network in 2014. Because the evidences showed that Yasouj due to uncontrolled growth of industrial and urban transport is subject to various air pollutants such as carbon monoxide and particulate matter. Overall, the results of the assessment and prediction of concentration of pollutants of Yasouj by artificial neural network showed that sigmoid transfer function to the hyperbolic tangent function is more efficient in measuring the concentration of pollutants.  

scholarly journals Neural networks in analysing 137Cs behaviour in the air in the Belgrade area

2014 ◽  
Vol 29 (3) ◽  
pp. 226-232
Author(s):  
Aleksandra Samolov ◽  
Snezana Dragovic ◽  
Marko Dakovic ◽  
Goran Bacic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Meteorological Parameters ◽  
Gamma Ray ◽  
Specific Activity ◽  
Meteorological Data ◽  
Principal Component ◽  
Spectrometric Analysis ◽  
Specific Activities ◽  
Artificial Neural

The application of the principal component analysis and artificial neural network method in forecasting 137Cs behaviour in the air as the function of meteorological parameters is presented. The model was optimized and tested using 137Cs specific activities obtained by standard gamma-ray spectrometric analysis of air samples collected in Belgrade (Serbia) during 2009-2011 and meteorological data for the same period. Low correlation (r = 0.20) between experimental values of 137Cs specific activities and those predicted by artificial neural network was obtained. This suggests that artificial neural network in the case of prediction of 137Cs specific activity, using temperature, insolation, and global Sun warming does not perform well, which can be explained by the relative independence of 137Cs specific activity of particular meteorological parameters and not by the ineffectiveness of artificial neural network in relating these parameters in general.

scholarly journals Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

2018 ◽  
Vol 12 (1) ◽  
pp. 738-749 ◽  
Author(s):  
Pezhman Taherei Ghazvinei ◽  
Hossein Hassanpour Darvishi ◽  
Amir Mosavi ◽  
Khamaruzaman bin Wan Yusof ◽  
Meysam Alizamir ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Extreme Learning Machine ◽  
Meteorological Parameters ◽  
Growth Prediction ◽  
Artificial Neural ◽  
Learning Machine

scholarly journals Tropospheric Ozone Formation Estimation in Urban City, Bangi, Using Artificial Neural Network (ANN)

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Fatin Aqilah Binti Abdul Aziz ◽  
Norliza Abd. Rahman ◽  
Jarinah Mohd Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ozone Concentration ◽  
Tropospheric Ozone ◽  
Air Pollutants ◽  
Rapid Development ◽  
Ground Level ◽  
Urban City ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Due to the rapid development of economy and society around the world, the most urban city is experiencing tropospheric ozone or commonly known as ground-level air pollutants. The concentration of air pollutants must be identified as an early precaution step by the local environmental or health agencies. This work aims to apply the artificial neural network (ANN) in estimating the ozone concentration forecast in Bangi. It consists of input variables such as temperature, relative humidity, concentration of nitrogen dioxide, time, UVA and UVB rays obtained from routine monitoring, and data recorded. Ten hidden layer is utilized to obtain the optimized ozone concentration, which is the output layer of the ANN framework. The finding showed that the meteorology condition and emission patterns play an important part in influencing the ozone concentration. However, a single network is sufficient enough to estimate the concentration despite any circumstances. Thus, it can be concluded that ANN is able to give reliable and satisfactory estimations of ozone concentration for the following day.

Atopic dermatitis severity during exposure to air pollutants and weather changes with an Artificial Neural Network (ANN) analysis

2020 ◽  
Vol 31 (8) ◽  
pp. 938-945 ◽  
Author(s):  
Vincenzo Patella ◽  
Giovanni Florio ◽  
Mario Palmieri ◽  
Jean Bousquet ◽  
Alessandro Tonacci ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Atopic Dermatitis ◽  
Air Pollutants ◽  
Atopic Dermatitis Severity ◽  
Artificial Neural ◽  
Weather Changes ◽  
Artificial Neural Network Ann
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