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A Comparative Study on Machine Learning Techniques for Benzene Prediction

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Futuristic Trends in Networks and Computing Technologies (FTNCT 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1206))

Abstract

Air pollution is one of the major public health issues confronting the world. The toxic levels of air pollutants in and around world are creating quite a menace. Detrimental air pollutants have accelerated the rate of cancer among human beings. Several studies have utilized different machine learning models for predicting air quality. In this paper, various machine learning based air quality monitoring techniques have been studied that can predict the concentration of benzene in air which is considered as one of the carcinogenic air pollutant. It has been observed that machine learning models have been extensively utilized in the various studies to reduce the prediction error rate. The overall objective of this paper is to compare well-known machine learning techniques which have been used to predict concentration of benzene in the atmosphere. Furthermore, the proposed ensemble based model for benzene prediction is also developed. The proposed technique is tested on well-known publicly available air pollution datasets for quantitative analysis. The proposed model achieves 91.56% of coefficient of determination and lower prediction error rate. In addition to these, performance metrics like Mean Absolute error (2%) and Root Mean Square error (3.1%) were estimated to determine the overall effectiveness of the proposed system. The proposed model is compared with the existing system and outcome of proposed model is improved. The comparative analysis of study shows that the proposed ensemble model performed better prediction results than the baseline existing machine learning models. Thus, it is well suitable to build effective benzene prediction model.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Lovely Professional University, Phagwara, India

    Veerawali Behal & Ramandeep Singh

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  1. Veerawali Behal
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  2. Ramandeep Singh
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Corresponding author

Correspondence to Veerawali Behal .

Editor information

Editors and Affiliations

  1. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    Pradeep Kumar Singh

  2. CDAC, Mohali, India

    Sanjay Sood

  3. Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Yugal Kumar

  4. Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

  5. Southern Federal University, Rostov-on-Don, Russia

    Anton Pljonkin

  6. Jiangsu Normal University, Xuzhou, China

    Wei-Chiang Hong

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Behal, V., Singh, R. (2020). A Comparative Study on Machine Learning Techniques for Benzene Prediction. In: Singh, P., Sood, S., Kumar, Y., Paprzycki, M., Pljonkin, A., Hong, WC. (eds) Futuristic Trends in Networks and Computing Technologies. FTNCT 2019. Communications in Computer and Information Science, vol 1206. Springer, Singapore. https://doi.org/10.1007/978-981-15-4451-4_45

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  • DOI: https://doi.org/10.1007/978-981-15-4451-4_45

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4450-7

  • Online ISBN: 978-981-15-4451-4

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