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Smart Sewage Water Management and Data Forecast
Kristianstad University, Faculty of Natural Sciences, Avdelningen för datavetenskap. Kristianstad University, Faculty of Natural Sciences, Research environment of Computer science (RECS).ORCID iD: 0000-0002-8032-6291
Kristianstads Kommun.
2021 (English)In: 33rd Workshop of the Swedish Artificial Intelligence Society, SAIS 2021, USA: Institute of Electrical and Electronics Engineers (IEEE), 2021, article id 9484017Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

There is currently an ongoing digital transformation for sewage and wastewater management. By automating data collection and enabling remote monitoring, we will not only be able to save abundant human resources but also enabling predictive maintenance which is based on big data analytics. This paper presents a smart sewage water management system which is currently under development in southern Sweden. Real-time data can be collected from over 500 sensors which have already been partially deployed. Preliminary data analysis shows that we can build statistical data models for ground water, rainfall, and sewage water flows, and use those models for data forecast and anomaly detection.

Place, publisher, year, edition, pages
USA: Institute of Electrical and Electronics Engineers (IEEE), 2021. article id 9484017
Keywords [en]
Anomaly detection, Big data, Internet of things, Sewage water management
National Category
Computer Sciences Computer Systems Information Systems
Identifiers
URN: urn:nbn:se:hkr:diva-22220DOI: 10.1109/SAIS53221.2021.9484017ISBN: 978-1-6654-4236-7 (electronic)OAI: oai:DiVA.org:hkr-22220DiVA, id: diva2:1581238
Conference
2021 Swedish Artificial Intelligence Society Workshop (SAIS), Luleå/Hybrid: Virtual, online, June 14-15, 2021
Projects
Smart realtids övervakat VA-system för uppmätt bräddning med direktkopplad dataanalys (Vinnova). Arbetet utförts inom Strategiska innovationsprogrammet IoT Sverige, en gemensam satsning av Vinnova, Formas och Energimyndigheten
Funder
Vinnova, 2020-04108
Note

This work is supported by the strategic innovation programme IoT Sweden, which is a joint effort by Vinnova, the Swedish research council Formas and the Swedish Energy Agency.

Available from: 2021-07-20 Created: 2021-07-20 Last updated: 2021-09-22Bibliographically approved

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Publisher's full texthttps://ieeexplore.ieee.org/document/9484017

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Wang, Qinghua

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CiteExportLink to record
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