Combining classifiers to detect faults in wastewater networks

Joshua Myrans, Zoran Kapelan, Richard Everson

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)


This work presents a methodology for automatic detection of structural faults in sewers from CCTV footage, which has been improved by combining the outputs of different machine learning techniques. The predictions of support vector machine and random forest classifiers are combined using three distinct techniques: 'both', 'most likely' and 'stacking'. Each technique is tested on CCTV data taken from real surveys covering a range of pipes at locations in the south-west of the UK. The best tested technique, stacking, offers a 5% increase in accuracy for minimal impact in efficiency, proving useful for future development and implementation of the fault detection methodology.

Original languageEnglish
Pages (from-to)2184-2189
Number of pages6
JournalWater Science and Technology
Issue number9
Publication statusPublished - 1 May 2018
Externally publishedYes


  • Classifier combination
  • Fault detection
  • Stacking

Fingerprint Dive into the research topics of 'Combining classifiers to detect faults in wastewater networks'. Together they form a unique fingerprint.

Cite this