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

Ojha, V. K. ORCID: https://orcid.org/0000-0002-9256-1192, Dutta, P. and Chaudhuri, A. (2017) Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study. Neural Computing and Applications, 28 (6). pp. 1343-1354. ISSN 0941-0643 doi: 10.1007/s00521-016-2443-0

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/82148
Item Type	Article
Refereed	Yes
Divisions	Interdisciplinary Research Centres (IDRCs) > Centre for the Mathematics of Planet Earth (CMPE) Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	Springer
Download/View statistics	View download statistics for this item