Air Pollution Modelling from Meteorological Parameters Using Artificial Neural Network

2018 ◽  
pp. 466-475 ◽  
Author(s):  
Sateesh N. Hosamane ◽  
G. P. Desai
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Artificial Neural Network ◽  
Meteorological Parameters ◽  
Air Pollution Modelling ◽  
Artificial Neural

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 Air pollution prediction by using an artificial neural network model

2019 ◽  
Vol 21 (6) ◽  
pp. 1341-1352 ◽  
Author(s):  
Heidar Maleki ◽  
Armin Sorooshian ◽  
Gholamreza Goudarzi ◽  
Zeynab Baboli ◽  
Yaser Tahmasebi Birgani ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Artificial Neural ◽  
Air Pollution Prediction ◽  
Pollution Prediction

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

Forecasting of Air Pollution Index with Artificial Neural Network

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Nur Haizum Abd Rahman ◽  
Muhammad Hisyam Lee ◽  
Mohd Talib Latif ◽  
Suhartono S.
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Time Series ◽  
Artificial Neural Network ◽  
Air Quality ◽  
Warning System ◽  
Pollution Index ◽  
Fuzzy Time Series ◽  
Air Pollution Index ◽  
Artificial Neural

In recent years, the arisen of air pollution in urban area address much attention globally. The air pollutants has emerged detrimental effects on health and living conditions. Time series forecasting is the important method nowadays with the ability to predict the future events. In this study, the forecasting is based on 10 years monthly data of Air Pollution Index (API) located in industrial and residential monitoring stations area in Malaysia. The autoregressive integrated moving average (ARIMA), fuzzy time series (FTS) and artificial neural network (ANNs) were used as the methods to forecast the API values. The performance of each method is compare using the root mean square error (RMSE). The result shows that the ANNs give the smallest forecasting error to forecast API compared to FTS and ARIMA. Therefore, the ANNs could be consider a reliable approach in early warning system to general public in understanding the air quality status that might effect their health and also in decision making processes for air quality control and management.

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