Abstract
Due to population growth and urbanization, air pollutant (AP) in environment is increasing day by day which creates a lot of health problem. AP data provide information about quality of air and health risk in surrounding which is important for environmental management. In this chapter, a review is made on air pollutant prediction using ANN techniques which are dependent on types of prediction intervals, i.e. monthly, daily and hourly. It is found that influence of different input variables, training algorithm and architecture changes prediction accuracy of ANN models.
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Abbreviations
- ANN:
-
Artificial Neural Network
- PCA:
-
Principle Component Analysis
- AP:
-
Air Pollutant
- PM:
-
Particulate Matters
- CO2:
-
Carbon di oxide
- RBF:
-
Radial Basis Function
- CO:
-
Carbon Monoxide
- R:
-
Regression
- D:
-
Day
- RH:
-
Relative humidity
- H:
-
Hour
- RSPM:
-
Respirable Suspended PM
- LM:
-
Levenberg Marquardt
- RMSE:
-
Root Mean Square Error
- MSE:
-
Mean Square Error
- SR:
-
Solar Radiation
- M:
-
Month
- SO2:
-
Sulphur di oxide
- MLR:
-
Multiple Linear Regression
- T:
-
Temperature
- NO:
-
Nitrogen Oxide
- TSP:
-
Total Suspended Particles
- NO2:
-
Nitrogen di oxide
- WD:
-
Wind Direction
- P:
-
Pressure
- WS:
-
Wind Speed
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Acknowledgements
We would like to thank the Department of Science and Technology (DST), New Delhi-110016 India for providing inspire fellowship with Ref. No. DST/INSPIRE Fellowship/2016/IF160676.
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Yadav, V., Nath, S. (2020). Novel Application of Artificial Neural Network Techniques for Prediction of Air Pollutants Using Stochastic Variables for Health Monitoring: A Review. In: Malik, H., Iqbal, A., Yadav, A. (eds) Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems. Advances in Intelligent Systems and Computing, vol 1096. Springer, Singapore. https://doi.org/10.1007/978-981-15-1532-3_10
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