Data-driven models for sewer blockage prediction

Mohammed Hassouna, Marta Reis, Mohamed Al Fairuz, Ali Tarhini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Water and waste utilities companies are under pressure to deliver a better service with a lower cost for consumers. It is important for these companies to understand all the factors that influence sewer blockages and be able to control them by prioritizing proactive strategies. These companies are keen to find solutions to reduce the occurrences of blockages on their wastewater network, which furthermore will help reduce maintenance costs, customer and environmental impact. This paper presents a data mining (DM) base approaches to predict Sewer blockages using absolute levels in EDMs (event duration monitors) and SLMs (sewer level monitors). Three different DM approaches are used (Decision Trees, Logistic Regression, and Random forest) to build the prediction models. The accuracy of these models is evaluated using real datasets containing blockage incident records for one of the biggest water and waste services providers in the UK, which will be denoted by Provider x in this research.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2019
EditorsMahdi H. Miraz, Peter S. Excell, Andrew Ware, Safeeullah Soomro, Maaruf Ali
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages68-72
Number of pages5
ISBN (Electronic)9781728121383
DOIs
Publication statusPublished - Aug 2019
Event2nd International Conference on Computing, Electronics and Communications Engineering, iCCECE 2019 - London, United Kingdom
Duration: Aug 22 2019Aug 23 2019

Publication series

NameProceedings - 2019 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2019

Conference

Conference2nd International Conference on Computing, Electronics and Communications Engineering, iCCECE 2019
Country/TerritoryUnited Kingdom
CityLondon
Period8/22/198/23/19

Keywords

  • Data analytic
  • Data mining
  • Sewer blockage

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Optimization
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

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