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Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study

  • Engineering Applications of Neural Networks
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Abstract

In this work, we formulated a real-world problem related to sewer pipeline gas detection using the classification-based approaches. The primary goal of this work was to identify the hazardousness of sewer pipeline to offer safe and non-hazardous access to sewer pipeline workers so that the human fatalities, which occurs due to the toxic exposure of sewer gas components, can be avoided. The dataset acquired through laboratory tests, experiments, and various literature sources was organized to design a predictive model that was able to identify/classify hazardous and non-hazardous situation of sewer pipeline. To design such prediction model, several classification algorithms were used and their performances were evaluated and compared, both empirically and statistically, over the collected dataset. In addition, the performances of several ensemble methods were analyzed to understand the extent of improvement offered by these methods. The result of this comprehensive study showed that the instance-based learning algorithm performed better than many other algorithms such as multilayer perceptron, radial basis function network, support vector machine, reduced pruning tree. Similarly, it was observed that multi-scheme ensemble approach enhanced the performance of base predictors.

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Acknowledgments

This work was supported by the IPROCOM Marie Curie Initial Training Network, funded through the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme.

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Authors and Affiliations

  1. IT4Innovations, VŠB Technical University of Ostrava, Ostrava, Czech Republic

    Varun Kumar Ojha

  2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Varun Kumar Ojha & Atal Chaudhuri

  3. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, India

    Paramartha Dutta

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  1. Varun Kumar Ojha
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  2. Paramartha Dutta
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  3. Atal Chaudhuri
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Correspondence to Varun Kumar Ojha.

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Ojha, V.K., Dutta, P. & Chaudhuri, A. Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study. Neural Comput & Applic 28, 1343–1354 (2017). https://doi.org/10.1007/s00521-016-2443-0

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  • DOI: https://doi.org/10.1007/s00521-016-2443-0

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