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Novel Application of Artificial Neural Network Techniques for Prediction of Air Pollutants Using Stochastic Variables for Health Monitoring: A Review

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Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1096))

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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Authors and Affiliations

  1. Department of Environmental Sciences and NRM, College of Forestry, Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad, 211007, Uttar Pradesh, India

    Vibha Yadav & Satyendra Nath

Authors
  1. Vibha Yadav
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  2. Satyendra Nath
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Corresponding author

Correspondence to Vibha Yadav .

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Editors and Affiliations

  1. Department of Instrumentation and Control Engineering (ICE), NSUT, Delhi, India

    Hasmat Malik

  2. Department of Electrical Engineering, Qatar University, Doha, Qatar

    Atif Iqbal

  3. Department of Electrical and Electronics Engineering (EEE), National Institute of Technology Sikkim, Sikkim, India

    Amit Kumar Yadav

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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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