Data-driven models for sewer blockage prediction

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

نتاج البحث: Conference contribution

5 اقتباسات (Scopus)

ملخص

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.

اللغة الأصليةEnglish
عنوان منشور المضيفProceedings - 2019 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2019
المحررونMahdi H. Miraz, Peter S. Excell, Andrew Ware, Safeeullah Soomro, Maaruf Ali
ناشرInstitute of Electrical and Electronics Engineers Inc.
الصفحات68-72
عدد الصفحات5
رقم المعيار الدولي للكتب (الإلكتروني)9781728121383
المعرِّفات الرقمية للأشياء
حالة النشرPublished - أغسطس 2019
الحدث2nd International Conference on Computing, Electronics and Communications Engineering, iCCECE 2019 - London, United Kingdom
المدة: أغسطس ٢٢ ٢٠١٩أغسطس ٢٣ ٢٠١٩

سلسلة المنشورات

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

Conference

Conference2nd International Conference on Computing, Electronics and Communications Engineering, iCCECE 2019
الدولة/الإقليمUnited Kingdom
المدينةLondon
المدة٨/٢٢/١٩٨/٢٣/١٩

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